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[ { "type": "text", "value": "I'm excited to share a really cool milestone in my AI/LLM journey.", "raw": "I'm excited to share a really cool milestone in my AI/LLM journey.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Brief backstory: Before diving into AI, I spent over a decade working in ecological fields such as the conservation corps, biodynamic farming, and natural habitat restoration. This background instilled in me a deep concern about the environmental impact of scaling AI without sustainable practices.", "raw": "Brief backstory: Before diving into AI, I spent over a decade working in ecological fields such as the conservation corps, biodynamic farming, and natural habitat restoration. This background instilled in me a deep concern about the environmental impact of scaling AI without sustainable practices.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power!", "raw": "Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "My current setup includes multiple linked Mac Pro tower desktops and custom code built from open-source libraries. While it's a bit experimental, this configuration is working great for my needs. All my LLM research, development, and client services now run exclusively on solar energy.", "raw": "My current setup includes multiple linked Mac Pro tower desktops and custom code built from open-source libraries. While it's a bit experimental, this configuration is working great for my needs. All my LLM research, development, and client services now run exclusively on solar energy.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'm curious if anyone else here has experimented with renewable energy for their LLM work?", "raw": "I'm curious if anyone else here has experimented with renewable energy for their LLM work?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For those interested in more details, I've written a brief blog post about this journey here ", "raw": "For those interested in more details, I've written a brief blog post about this journey here ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://medium.com/@betalabsllm/powering-the-future-be-ta-labs-revolutionary-100-solar-powered-ai-operation-444433e61d43", "href": "https://medium.com/@betalabsllm/powering-the-future-be-ta-labs-revolutionary-100-solar-powered-ai-operation-444433e61d43", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I'm excited to share a really cool milestone in my AI/LLM journey. Brief backstory: Before diving into AI, I spent over a decade working in ecological fields such as the conservation corps, biodynamic farming, and natural habitat restoration. This background instilled in me a deep concern about the environmental impact of scaling AI without sustainable practices. Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power! My current setup includes multiple linked Mac Pro tower desktops and custom code built from open-source libraries. While it's a bit experimental, this configuration is working great for my needs. All my LLM research, development, and client services now run exclusively on solar energy. I'm curious if anyone else here has experimented with renewable energy for their LLM work? For those interested in more details, I've written a brief blog post about this journey here https://medium.com/@betalabsllm/powering-the-future-be-ta-labs-revolutionary-100-solar-powered-ai-operation-444433e61d43
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2024-08-18T09:00:52.000Z
2024-08-19T09:55:47.551Z
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[ { "type": "text", "value": "How good are you at spotting AI-generated images? ", "raw": "How good are you at spotting AI-generated images? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Find out by playing Fake Insects 🐞 a Game where you need to identify which insects are fake (AI generated). Good luck & share your best score in the comments! ", "raw": "Find out by playing Fake Insects 🐞 a Game where you need to identify which insects are fake (AI generated). Good luck & share your best score in the comments! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/victor/fake-insects", "href": null, "resource": { "type": "space", "id": "victor/fake-insects", "discussionNum": null }, "url": "https://huggingface.co/spaces/victor/fake-insects", "code": null, "user": null, "label": null, "lang": null } ]
How good are you at spotting AI-generated images? Find out by playing Fake Insects 🐞 a Game where you need to identify which insects are fake (AI generated). Good luck & share your best score in the comments! https://huggingface.co/spaces/victor/fake-insects
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2024-08-17T18:25:03.000Z
2024-08-25T16:20:29.158Z
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[ { "type": "text", "value": "So turns out I've been spreading a bit of misinformation when it comes to imatrix in llama.cpp", "raw": "So turns out I've been spreading a bit of misinformation when it comes to imatrix in llama.cpp", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It starts true; imatrix runs the model against a corpus of text and tracks the activation of weights to determine which are most important", "raw": "It starts true; imatrix runs the model against a corpus of text and tracks the activation of weights to determine which are most important", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "However what the quantization then does with that information is where I was wrong.", "raw": "However what the quantization then does with that information is where I was wrong.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I think I made the accidental connection between imatrix and exllamav2's measuring, where ExLlamaV2 decides how many bits to assign to which weight depending on the goal BPW", "raw": "I think I made the accidental connection between imatrix and exllamav2's measuring, where ExLlamaV2 decides how many bits to assign to which weight depending on the goal BPW", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Instead, what llama.cpp with imatrix does is it attempts to select a scale for a quantization block that most accurately returns the important weights to their original values, ie minimizing the dequantization error based on the importance of activations", "raw": "Instead, what llama.cpp with imatrix does is it attempts to select a scale for a quantization block that most accurately returns the important weights to their original values, ie minimizing the dequantization error based on the importance of activations", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The mildly surprising part is that it actually just does a relatively brute force search, it picks a bunch of scales and tries each and sees which one results in the minimum error for weights deemed important in the group", "raw": "The mildly surprising part is that it actually just does a relatively brute force search, it picks a bunch of scales and tries each and sees which one results in the minimum error for weights deemed important in the group", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But yeah, turns out, the quantization scheme is always the same, it's just that the scaling has a bit more logic to it when you use imatrix", "raw": "But yeah, turns out, the quantization scheme is always the same, it's just that the scaling has a bit more logic to it when you use imatrix", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Huge shoutout to ", "raw": "Huge shoutout to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@compilade", "href": null, "resource": null, "url": null, "code": null, "user": "compilade", "label": null, "lang": null }, { "type": "text", "value": " for helping me wrap my head around it - feel free to add/correct as well if I've messed something up", "raw": " for helping me wrap my head around it - feel free to add/correct as well if I've messed something up", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
So turns out I've been spreading a bit of misinformation when it comes to imatrix in llama.cpp It starts true; imatrix runs the model against a corpus of text and tracks the activation of weights to determine which are most important However what the quantization then does with that information is where I was wrong. I think I made the accidental connection between imatrix and exllamav2's measuring, where ExLlamaV2 decides how many bits to assign to which weight depending on the goal BPW Instead, what llama.cpp with imatrix does is it attempts to select a scale for a quantization block that most accurately returns the important weights to their original values, ie minimizing the dequantization error based on the importance of activations The mildly surprising part is that it actually just does a relatively brute force search, it picks a bunch of scales and tries each and sees which one results in the minimum error for weights deemed important in the group But yeah, turns out, the quantization scheme is always the same, it's just that the scaling has a bit more logic to it when you use imatrix Huge shoutout to @compilade for helping me wrap my head around it - feel free to add/correct as well if I've messed something up
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2024-08-17T12:41:03.000Z
2024-08-24T07:22:10.246Z
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[ { "type": "text", "value": "I know a secret about knowledge graphs that the world doesn't! There are severe mathematical limitations to geometric fractals. It is classed as an 'unsolvable problem' in the mathematics world. There are currently ~1,000 mathematicians or so in the world that give this problem serious thought. You literally cannot solve it with geometric fractals. This is why I invented P-FAF, it uses calculus based fractals instead. I literally invented the math to make it work. I solved an 'unsolvable equation' to make the math work. You ONLY make the math work the way I did it in the end. I have never released the licensing commercially. Good luck!", "raw": "I know a secret about knowledge graphs that the world doesn't! There are severe mathematical limitations to geometric fractals. It is classed as an 'unsolvable problem' in the mathematics world. There are currently ~1,000 mathematicians or so in the world that give this problem serious thought. You literally cannot solve it with geometric fractals. This is why I invented P-FAF, it uses calculus based fractals instead. I literally invented the math to make it work. I solved an 'unsolvable equation' to make the math work. You ONLY make the math work the way I did it in the end. I have never released the licensing commercially. Good luck!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I know a secret about knowledge graphs that the world doesn't! There are severe mathematical limitations to geometric fractals. It is classed as an 'unsolvable problem' in the mathematics world. There are currently ~1,000 mathematicians or so in the world that give this problem serious thought. You literally cannot solve it with geometric fractals. This is why I invented P-FAF, it uses calculus based fractals instead. I literally invented the math to make it work. I solved an 'unsolvable equation' to make the math work. You ONLY make the math work the way I did it in the end. I have never released the licensing commercially. Good luck!
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2024-08-17T09:47:28.000Z
2024-08-17T09:59:45.121Z
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/posts/TuringsSolutions/860006486706088
573
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[ { "type": "text", "value": "Supercool Weekend Read🤖", "raw": "Supercool Weekend Read🤖", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.", "raw": "Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Details on Techniques (Simplified):", "raw": "Details on Techniques (Simplified):", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They started off with a large pre-trained language model (15B params), then:", "raw": "They started off with a large pre-trained language model (15B params), then:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.", "raw": "1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Pruned (remove) less important parts of the model to reduce its size.", "raw": "2. Pruned (remove) less important parts of the model to reduce its size.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. Retrained the pruned model using knowledge distillation, where the original large model acts as a teacher for the smaller pruned model.", "raw": "3. Retrained the pruned model using knowledge distillation, where the original large model acts as a teacher for the smaller pruned model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4. Used a lightweight neural architecture search to find the best configuration for the pruned model.", "raw": "4. Used a lightweight neural architecture search to find the best configuration for the pruned model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5. Repeated this process iteratively to create even smaller models.", "raw": "5. Repeated this process iteratively to create even smaller models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Cool, giving it a try this weekend 😎", "raw": "Cool, giving it a try this weekend 😎", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/NVlabs/Minitron", "href": "https://github.com/NVlabs/Minitron", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2407.14679", "href": "https://arxiv.org/abs/2407.14679", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo: ", "raw": "Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/nvidia/minitron", "href": null, "resource": { "type": "space", "id": "nvidia/minitron", "discussionNum": null }, "url": "https://huggingface.co/spaces/nvidia/minitron", "code": null, "user": null, "label": null, "lang": null } ]
Supercool Weekend Read🤖 Nvidia researchers achieved SOTA LLM compression metrics using pruning and knowledge distillation techniques. Details on Techniques (Simplified): They started off with a large pre-trained language model (15B params), then: 1. Estimated the importance of different parts of the model (neurons, attention heads, layers) using activation-based metrics on a small calibration dataset. 2. Pruned (remove) less important parts of the model to reduce its size. 3. Retrained the pruned model using knowledge distillation, where the original large model acts as a teacher for the smaller pruned model. 4. Used a lightweight neural architecture search to find the best configuration for the pruned model. 5. Repeated this process iteratively to create even smaller models. Cool, giving it a try this weekend 😎 Code: https://github.com/NVlabs/Minitron Paper: https://arxiv.org/abs/2407.14679 Demo: https://huggingface.co/spaces/nvidia/minitron
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[]
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2024-08-17T03:42:44.000Z
2024-08-18T03:10:40.798Z
[]
/posts/Jaward/864345037222506
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[ { "type": "text", "value": "Added FLUX.1 pro/dev/schnell and AuraFlow v0.2 to ", "raw": "Added FLUX.1 pro/dev/schnell and AuraFlow v0.2 to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/fal/imgsys", "href": null, "resource": { "type": "space", "id": "fal/imgsys", "discussionNum": null }, "url": "https://huggingface.co/spaces/fal/imgsys", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " !!! Go play with it and get us some votez", "raw": " !!! Go play with it and get us some votez", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Added FLUX.1 pro/dev/schnell and AuraFlow v0.2 to https://huggingface.co/spaces/fal/imgsys !!! Go play with it and get us some votez
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2024-08-17T03:42:22.000Z
2024-08-17T03:42:35.601Z
[]
/posts/isidentical/970159160979953
589
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529523271915020
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NEW math-instruct model + dataset! https://huggingface.co/ValiantLabs/Llama3.1-8B-Cobalt is our new math-instruct model. Trained using a synthetic math-instruct dataset generated with Llama 3.1 405b. Find the dataset here: https://huggingface.co/datasets/sequelbox/Polytope More to come soon :)
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2024-08-16T16:38:07.000Z
2024-08-16T16:38:07.863Z
[]
/posts/sequelbox/529523271915020
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[ { "type": "text", "value": "Introducing HelpingAI2-9B, an emotionally intelligent LLM. ", "raw": "Introducing HelpingAI2-9B, an emotionally intelligent LLM. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model Link : ", "raw": "Model Link : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/OEvortex/HelpingAI2-9B", "href": null, "resource": { "type": "model", "id": "OEvortex/HelpingAI2-9B", "discussionNum": null }, "url": "https://huggingface.co/OEvortex/HelpingAI2-9B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo Link: ", "raw": "Demo Link: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Abhaykoul/HelpingAI2", "href": null, "resource": { "type": "space", "id": "Abhaykoul/HelpingAI2", "discussionNum": null }, "url": "https://huggingface.co/spaces/Abhaykoul/HelpingAI2", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding.", "raw": "This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Key Features:", "raw": "Key Features:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "-----------------", "raw": "-----------------", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition.", "raw": "* It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* It gives responses in empathetic and supportive manner.", "raw": "* It gives responses in empathetic and supportive manner.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Must try our demo: ", "raw": "Must try our demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Abhaykoul/HelpingAI2", "href": null, "resource": { "type": "space", "id": "Abhaykoul/HelpingAI2", "discussionNum": null }, "url": "https://huggingface.co/spaces/Abhaykoul/HelpingAI2", "code": null, "user": null, "label": null, "lang": null } ]
Introducing HelpingAI2-9B, an emotionally intelligent LLM. Model Link : https://huggingface.co/OEvortex/HelpingAI2-9B Demo Link: https://huggingface.co/spaces/Abhaykoul/HelpingAI2 This model is part of the innovative HelpingAI series and it stands out for its ability to engage users with emotional understanding. Key Features: ----------------- * It gets 95.89 score on EQ Bench greather than all top notch LLMs, reflecting advanced emotional recognition. * It gives responses in empathetic and supportive manner. Must try our demo: https://huggingface.co/spaces/Abhaykoul/HelpingAI2
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2024-08-16T11:38:23.000Z
2024-08-16T11:38:23.841Z
[]
/posts/Abhaykoul/972313929139427
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[ { "type": "text", "value": "Announcing another BIG data drop! This time it's ~275M images from Flickr ", "raw": "Announcing another BIG data drop! This time it's ~275M images from Flickr ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/bigdata-pw/Flickr", "href": null, "resource": { "type": "dataset", "id": "bigdata-pw/Flickr", "discussionNum": null }, "url": "https://huggingface.co/datasets/bigdata-pw/Flickr", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Data acquisition for this project is still in progress, get ready for an update soon:tm: ", "raw": "Data acquisition for this project is still in progress, get ready for an update soon:tm: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In case you missed them; other BIG data drops include Diffusion1B ", "raw": "In case you missed them; other BIG data drops include Diffusion1B ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/bigdata-pw/Diffusion1B", "href": null, "resource": { "type": "dataset", "id": "bigdata-pw/Diffusion1B", "discussionNum": null }, "url": "https://huggingface.co/datasets/bigdata-pw/Diffusion1B", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - ~1.23B images and generation parameters from a variety of diffusion models and if you fancy practicing diffusion model training check out Dataception ", "raw": " - ~1.23B images and generation parameters from a variety of diffusion models and if you fancy practicing diffusion model training check out Dataception ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/bigdata-pw/Dataception", "href": null, "resource": { "type": "dataset", "id": "bigdata-pw/Dataception", "discussionNum": null }, "url": "https://huggingface.co/datasets/bigdata-pw/Dataception", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - a dataset of over 5000 datasets in WebDataset format!", "raw": " - a dataset of over 5000 datasets in WebDataset format!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Requests are always welcome so reach out if there's a dataset you'd like to see!", "raw": "Requests are always welcome so reach out if there's a dataset you'd like to see!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Announcing another BIG data drop! This time it's ~275M images from Flickr https://huggingface.co/datasets/bigdata-pw/Flickr Data acquisition for this project is still in progress, get ready for an update soon:tm: In case you missed them; other BIG data drops include Diffusion1B https://huggingface.co/datasets/bigdata-pw/Diffusion1B - ~1.23B images and generation parameters from a variety of diffusion models and if you fancy practicing diffusion model training check out Dataception https://huggingface.co/datasets/bigdata-pw/Dataception - a dataset of over 5000 datasets in WebDataset format! Requests are always welcome so reach out if there's a dataset you'd like to see!
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2024-08-15T18:55:29.000Z
2024-08-16T09:17:26.971Z
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/posts/hlky/343314951558641
1,911
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576586250383339
[ { "type": "text", "value": "✨ Feeling thankful... ", "raw": "✨ Feeling thankful... ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🇮🇳 15th August, 2024; on India's 78th Independence Day ", "raw": "🇮🇳 15th August, 2024; on India's 78th Independence Day ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🎉 Crossed 100 followers on Hugging Face", "raw": "🎉 Crossed 100 followers on Hugging Face", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🏆 Got LinkedIn Top Voice", "raw": "🏆 Got LinkedIn Top Voice", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🤖 AI has never been more exciting and I am here for it", "raw": "🤖 AI has never been more exciting and I am here for it", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👀 ", "raw": "👀 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@clem", "href": null, "resource": null, "url": null, "code": null, "user": "clem", "label": null, "lang": null }, { "type": "text", "value": " Can I be a Hugging Face fellow now? ", "raw": " Can I be a Hugging Face fellow now? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
✨ Feeling thankful... 🇮🇳 15th August, 2024; on India's 78th Independence Day 🎉 Crossed 100 followers on Hugging Face 🏆 Got LinkedIn Top Voice 🤖 AI has never been more exciting and I am here for it 👀 @clem Can I be a Hugging Face fellow now?
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2024-08-15T18:29:10.000Z
2024-08-15T18:29:10.232Z
[]
/posts/singhsidhukuldeep/576586250383339
1,699
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780629919182270
[ { "type": "text", "value": "Improved ControlNet! ", "raw": "Improved ControlNet! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now supports dynamic resolution for perfect landscape and portrait outputs. Generate stunning images without distortion—optimized for any aspect ratio!", "raw": "Now supports dynamic resolution for perfect landscape and portrait outputs. Generate stunning images without distortion—optimized for any aspect ratio!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "...", "raw": "...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny", "href": null, "resource": { "type": "space", "id": "DamarJati/FLUX.1-DEV-Canny", "discussionNum": null }, "url": "https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny", "code": null, "user": null, "label": null, "lang": null } ]
Improved ControlNet! Now supports dynamic resolution for perfect landscape and portrait outputs. Generate stunning images without distortion—optimized for any aspect ratio! ... https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny
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2024-08-15T15:17:57.000Z
2024-08-15T15:49:32.401Z
[]
/posts/DamarJati/780629919182270
3,164
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608656345183499
[ { "type": "text", "value": "As some of you know, I try to convert models to either fp32 or bf16 depending on theirs size before doing imatrix and quantization", "raw": "As some of you know, I try to convert models to either fp32 or bf16 depending on theirs size before doing imatrix and quantization", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Today I decided to see if that matters, and the results have me.. for lack of a better word, perplexed", "raw": "Today I decided to see if that matters, and the results have me.. for lack of a better word, perplexed", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "My setup:", "raw": "My setup:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Mistral Nemo Instruct 2407", "raw": "Mistral Nemo Instruct 2407", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- convert to FP32, calculate imatrix, quantize to Q8_0 and Q4_K_M", "raw": "- convert to FP32, calculate imatrix, quantize to Q8_0 and Q4_K_M", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- convert to FP16, calculate imatrix, quantize to Q8_0 and Q4_K_M", "raw": "- convert to FP16, calculate imatrix, quantize to Q8_0 and Q4_K_M", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I calculated the kld base from the FP32 model:", "raw": "I calculated the kld base from the FP32 model:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-f32.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld -ngl 35 -fa -sm row`", "href": null, "resource": null, "url": null, "code": "./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-f32.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld -ngl 35 -fa -sm row", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "then calculated the divergence itself for each like so:", "raw": "then calculated the divergence itself for each like so:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-Q8_0.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld --kl-divergence -ngl 50 -fa -sm row`", "href": null, "resource": null, "url": null, "code": "./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-Q8_0.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld --kl-divergence -ngl 50 -fa -sm row", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Q4_K_M from fp16 and fp32 were similar, trading blows across statistics, odd since i expected fp32 to be strictly better but it's not", "raw": "Q4_K_M from fp16 and fp32 were similar, trading blows across statistics, odd since i expected fp32 to be strictly better but it's not", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Q8_0 is where things get weird. Despite each file being slightly different size, and the sha256sum of course being different, they each get *completely identical* scores, down to 6 decimal places of precision on the statistics.", "raw": "Q8_0 is where things get weird. Despite each file being slightly different size, and the sha256sum of course being different, they each get *completely identical* scores, down to 6 decimal places of precision on the statistics.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How is this possible? Is there something I don't understand about llama.cpp that makes it always convert to fp16 before it does quantization? Am I wasting time using FP32/BF16??", "raw": "How is this possible? Is there something I don't understand about llama.cpp that makes it always convert to fp16 before it does quantization? Am I wasting time using FP32/BF16??", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
As some of you know, I try to convert models to either fp32 or bf16 depending on theirs size before doing imatrix and quantization Today I decided to see if that matters, and the results have me.. for lack of a better word, perplexed My setup: Mistral Nemo Instruct 2407 - convert to FP32, calculate imatrix, quantize to Q8_0 and Q4_K_M - convert to FP16, calculate imatrix, quantize to Q8_0 and Q4_K_M I calculated the kld base from the FP32 model: `./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-f32.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld -ngl 35 -fa -sm row` then calculated the divergence itself for each like so: `./llama-perplexity -m /models/Mistral-Nemo-Instruct-2407-Q8_0.gguf -f /training_data/wikitext-2-raw/wiki.test.raw --kl-divergence-base /training_data/mistral-nemo-f32.kld --kl-divergence -ngl 50 -fa -sm row` Q4_K_M from fp16 and fp32 were similar, trading blows across statistics, odd since i expected fp32 to be strictly better but it's not Q8_0 is where things get weird. Despite each file being slightly different size, and the sha256sum of course being different, they each get *completely identical* scores, down to 6 decimal places of precision on the statistics. How is this possible? Is there something I don't understand about llama.cpp that makes it always convert to fp16 before it does quantization? Am I wasting time using FP32/BF16??
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2024-08-15T14:21:20.000Z
2024-08-17T07:15:35.631Z
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/posts/bartowski/608656345183499
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https://huggingface.co/fal/AuraFlow-v0.3 is now here with support for different aspect resolutions (w/h up to 1536px!) and much nicer aesthetics! Make sure to install the latest diffusers to get support for it.
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2024-08-15T14:15:17.000Z
2024-08-15T14:15:17.634Z
[]
/posts/isidentical/574566841162028
1,769
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[ { "type": "text", "value": "𝗭𝗲𝗿𝗼-𝗺𝗮𝘁𝗵 𝗶𝗻𝘁𝗿𝗼 𝘁𝗼 𝗔𝗜 𝗵𝗶𝘀𝘁𝗼𝗿𝘆: 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝟭𝟵𝟱𝟬𝘀 𝘁𝗼 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗟𝗟𝗠𝘀 📖", "raw": "𝗭𝗲𝗿𝗼-𝗺𝗮𝘁𝗵 𝗶𝗻𝘁𝗿𝗼 𝘁𝗼 𝗔𝗜 𝗵𝗶𝘀𝘁𝗼𝗿𝘆: 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝟭𝟵𝟱𝟬𝘀 𝘁𝗼 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗟𝗟𝗠𝘀 📖", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I wanted to structure my thinking about LLMs by going through their history since the 50s. This history is captivating, with the opposition between Connexionists (Rosenblatt, LeCun) and Symbolists, the first victories of \"deep\" neural networks, the revolution of Attention...", "raw": "I wanted to structure my thinking about LLMs by going through their history since the 50s. This history is captivating, with the opposition between Connexionists (Rosenblatt, LeCun) and Symbolists, the first victories of \"deep\" neural networks, the revolution of Attention...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "So I might have gone a bit too far! 😅", "raw": "So I might have gone a bit too far! 😅", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📝 I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers...", "raw": "📝 I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "✅ And I've made sure to keep it 100% horrible-latex-math-free: the technical stuff is conveyed in graphs only, so it should be accessible to really anyone, even your grandfather (I'm sending it to mine right now).", "raw": "✅ And I've made sure to keep it 100% horrible-latex-math-free: the technical stuff is conveyed in graphs only, so it should be accessible to really anyone, even your grandfather (I'm sending it to mine right now).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read it here in english 👉 ", "raw": "Read it here in english 👉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://aymeric-roucher.github.io/brief-history-of-ai/", "href": "https://aymeric-roucher.github.io/brief-history-of-ai/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Pour le post en français 👉 ", "raw": "Pour le post en français 👉 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://aymeric-roucher.github.io/breve-histoire-de-l-ia/", "href": "https://aymeric-roucher.github.io/breve-histoire-de-l-ia/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
𝗭𝗲𝗿𝗼-𝗺𝗮𝘁𝗵 𝗶𝗻𝘁𝗿𝗼 𝘁𝗼 𝗔𝗜 𝗵𝗶𝘀𝘁𝗼𝗿𝘆: 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝟭𝟵𝟱𝟬𝘀 𝘁𝗼 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗟𝗟𝗠𝘀 📖 I wanted to structure my thinking about LLMs by going through their history since the 50s. This history is captivating, with the opposition between Connexionists (Rosenblatt, LeCun) and Symbolists, the first victories of "deep" neural networks, the revolution of Attention... So I might have gone a bit too far! 😅 📝 I've made a long post summarizing the main stages of building LLMs: neural networks, optimization, backpropagation, attention layers... ✅ And I've made sure to keep it 100% horrible-latex-math-free: the technical stuff is conveyed in graphs only, so it should be accessible to really anyone, even your grandfather (I'm sending it to mine right now). Read it here in english 👉 https://aymeric-roucher.github.io/brief-history-of-ai/ Pour le post en français 👉 https://aymeric-roucher.github.io/breve-histoire-de-l-ia/
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2024-08-15T13:59:58.000Z
2024-08-15T13:59:58.470Z
[]
/posts/m-ric/541848107798204
993
0
420890568934318
[ { "type": "text", "value": "Some personal and professional news ✨", "raw": "Some personal and professional news ✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'm writing a book on ML metrics.", "raw": "I'm writing a book on ML metrics.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Together with Wojtek Kuberski, we’re creating the missing piece of every ML university program and online course: a book solely dedicated to Machine Learning metrics!", "raw": "Together with Wojtek Kuberski, we’re creating the missing piece of every ML university program and online course: a book solely dedicated to Machine Learning metrics!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The book will cover the following types of metrics:", "raw": "The book will cover the following types of metrics:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• Regression", "raw": "• Regression", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• Classification", "raw": "• Classification", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• Clustering", "raw": "• Clustering", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• Ranking", "raw": "• Ranking", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• Vision", "raw": "• Vision", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• Text", "raw": "• Text", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• GenAI", "raw": "• GenAI", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "• Bias and Fairness", "raw": "• Bias and Fairness", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👉 check out the book: ", "raw": "👉 check out the book: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.nannyml.com/metrics", "href": "https://www.nannyml.com/metrics", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Some personal and professional news ✨ I'm writing a book on ML metrics. Together with Wojtek Kuberski, we’re creating the missing piece of every ML university program and online course: a book solely dedicated to Machine Learning metrics! The book will cover the following types of metrics: • Regression • Classification • Clustering • Ranking • Vision • Text • GenAI • Bias and Fairness 👉 check out the book: https://www.nannyml.com/metrics
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2024-08-15T13:04:54.000Z
2024-08-15T14:41:01.073Z
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/posts/santiviquez/420890568934318
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[ { "type": "text", "value": "Everchanging Quest is out !", "raw": "Everchanging Quest is out !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements.", "raw": "It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Come test it on the space :", "raw": "Come test it on the space :", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Jofthomas/Everchanging-Quest", "href": null, "resource": { "type": "space", "id": "Jofthomas/Everchanging-Quest", "discussionNum": null }, "url": "https://huggingface.co/spaces/Jofthomas/Everchanging-Quest", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Everchanging Quest is out ! It is an LLM controlled Rogue-Like in which the LLM gets a markdown representation of the map, and should generate a JSON with the objective to fulfill on the map as well as the necessary objects and their placements. Come test it on the space : https://huggingface.co/spaces/Jofthomas/Everchanging-Quest
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2024-08-15T09:32:15.000Z
2024-09-04T20:40:33.377Z
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/posts/Jofthomas/525100587174350
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ehm, so today i've finished my new project https://huggingface.co/spaces/Blane187/animalese-py or you can make your voice to animalese with it: https://huggingface.co/spaces/Blane187/animalese_RVC i'm just bored, so i make the project, lol
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2024-08-15T09:06:34.000Z
2024-08-15T09:06:34.270Z
[]
/posts/Blane187/240020635038154
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[ { "type": "text", "value": "📣 Introducing Dataset Viber: your chill repo for data collection, annotation and vibe checks! 🎉", "raw": "📣 Introducing Dataset Viber: your chill repo for data collection, annotation and vibe checks! 🎉", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I've cooked up Dataset Viber, a set of cool tools designed to make data preparation for AI models easier, more approachable and enjoyable for standalone AI engineers and enthusiasts.", "raw": "I've cooked up Dataset Viber, a set of cool tools designed to make data preparation for AI models easier, more approachable and enjoyable for standalone AI engineers and enthusiasts.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔧 What Dataset Viber offers:", "raw": "🔧 What Dataset Viber offers:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- CollectorInterface: Lazily collect model interaction data without human annotation", "raw": "- CollectorInterface: Lazily collect model interaction data without human annotation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- AnnotatorInterface: Annotate your data with models in the loop", "raw": "- AnnotatorInterface: Annotate your data with models in the loop", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- BulkInterface: Explore data distribution and annotate in bulk", "raw": "- BulkInterface: Explore data distribution and annotate in bulk", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Embedder: Efficiently embed data with ONNX-optimized speeds", "raw": "- Embedder: Efficiently embed data with ONNX-optimized speeds", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🎯 Key features:", "raw": "🎯 Key features:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Supports various tasks for text, chat, and image modalities", "raw": "- Supports various tasks for text, chat, and image modalities", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Runs in .ipynb notebooks", "raw": "- Runs in .ipynb notebooks", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Logs data to local CSV or directly to Hugging Face Hub", "raw": "- Logs data to local CSV or directly to Hugging Face Hub", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Easy to install via pip: ", "raw": "- Easy to install via pip: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`pip install dataset-viber`", "href": null, "resource": null, "url": null, "code": "pip install dataset-viber", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's not designed for team collaboration or production use, but rather as a fun and efficient toolkit for individual projects.", "raw": "It's not designed for team collaboration or production use, but rather as a fun and efficient toolkit for individual projects.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Want to give it a try? Check out the repository link ", "raw": "Want to give it a try? Check out the repository link ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/davidberenstein1957/dataset-viber/", "href": "https://github.com/davidberenstein1957/dataset-viber/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'm excited to hear your feedback and learn how you vibe with your data. Feel free to open an issue or reach out if you have any questions or suggestions!", "raw": "I'm excited to hear your feedback and learn how you vibe with your data. Feel free to open an issue or reach out if you have any questions or suggestions!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Some shoutouts:", "raw": "Some shoutouts:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Gradio for the amazing backbone", "raw": "- Gradio for the amazing backbone", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Daniel van Strien for some initial presentations I did on vibe checks", "raw": "- Daniel van Strien for some initial presentations I did on vibe checks", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Emily Omier for the workshop on structuring GitHub repo READMEs", "raw": "- Emily Omier for the workshop on structuring GitHub repo READMEs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Hamel Husain for keeping mentioning that people should look at their data.", "raw": "- Hamel Husain for keeping mentioning that people should look at their data.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Philipp Schmid for his code for ONNX feature-extractors", "raw": "- Philipp Schmid for his code for ONNX feature-extractors", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Ben Burtenshaw for the first PR", "raw": "- Ben Burtenshaw for the first PR", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
📣 Introducing Dataset Viber: your chill repo for data collection, annotation and vibe checks! 🎉 I've cooked up Dataset Viber, a set of cool tools designed to make data preparation for AI models easier, more approachable and enjoyable for standalone AI engineers and enthusiasts. 🔧 What Dataset Viber offers: - CollectorInterface: Lazily collect model interaction data without human annotation - AnnotatorInterface: Annotate your data with models in the loop - BulkInterface: Explore data distribution and annotate in bulk - Embedder: Efficiently embed data with ONNX-optimized speeds 🎯 Key features: - Supports various tasks for text, chat, and image modalities - Runs in .ipynb notebooks - Logs data to local CSV or directly to Hugging Face Hub - Easy to install via pip: `pip install dataset-viber` It's not designed for team collaboration or production use, but rather as a fun and efficient toolkit for individual projects. Want to give it a try? Check out the repository link https://github.com/davidberenstein1957/dataset-viber/. I'm excited to hear your feedback and learn how you vibe with your data. Feel free to open an issue or reach out if you have any questions or suggestions! Some shoutouts: - Gradio for the amazing backbone - Daniel van Strien for some initial presentations I did on vibe checks - Emily Omier for the workshop on structuring GitHub repo READMEs - Hamel Husain for keeping mentioning that people should look at their data. - Philipp Schmid for his code for ONNX feature-extractors - Ben Burtenshaw for the first PR
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2024-08-15T07:54:24.000Z
2024-08-15T12:28:33.093Z
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/posts/davidberenstein1957/575318853216493
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Here is a hackable and minimal implementation showing how to perform distributed text-to-image generation with Diffusers and Accelerate. Full snippet is here: https://gist.github.com/sayakpaul/cfaebd221820d7b43fae638b4dfa01ba With @JW17
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2024-08-15T06:53:12.000Z
2024-08-15T06:53:12.559Z
[]
/posts/sayakpaul/610534902245547
2,931
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343123848597732
[ { "type": "text", "value": "hey everyone, if you have any experience with fine tuning ai models please feel free to reach out to me :)", "raw": "hey everyone, if you have any experience with fine tuning ai models please feel free to reach out to me :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We're looking for devs to fine tune an AI model for a snapchat chatting AI project. Open to either part time or full time work.", "raw": "We're looking for devs to fine tune an AI model for a snapchat chatting AI project. Open to either part time or full time work.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Email: [email protected]", "raw": "Email: [email protected]", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Telegram: Kirann46", "raw": "Telegram: Kirann46", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
hey everyone, if you have any experience with fine tuning ai models please feel free to reach out to me :) We're looking for devs to fine tune an AI model for a snapchat chatting AI project. Open to either part time or full time work. Email: [email protected] Telegram: Kirann46
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[]
[]
2024-08-15T02:04:31.000Z
2024-08-15T02:04:31.793Z
[]
/posts/risestud/343123848597732
463
0
310082664847975
[ { "type": "text", "value": "🚀 Introducing ChemVLM, the first open-source multimodal large language model dedicated to chemistry!", "raw": "🚀 Introducing ChemVLM, the first open-source multimodal large language model dedicated to chemistry!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🌟Comparable performances with commercial models or specific OCR model but with dialogue capabilities!", "raw": "🌟Comparable performances with commercial models or specific OCR model but with dialogue capabilities!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "✨2B/26B Models Here! ", "raw": "✨2B/26B Models Here! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/AI4Chem/ChemVLM-26B", "href": null, "resource": { "type": "model", "id": "AI4Chem/ChemVLM-26B", "discussionNum": null }, "url": "https://huggingface.co/AI4Chem/ChemVLM-26B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2408.07246", "href": null, "resource": { "type": "paper", "id": "2408.07246", "discussionNum": null }, "url": "https://huggingface.co/papers/2408.07246", "code": null, "user": null, "label": "Seeing and Understanding: Bridging Vision with Chemical Knowledge Via\n ChemVLM (2408.07246)", "lang": null } ]
🚀 Introducing ChemVLM, the first open-source multimodal large language model dedicated to chemistry! 🌟Comparable performances with commercial models or specific OCR model but with dialogue capabilities! ✨2B/26B Models Here! https://huggingface.co/AI4Chem/ChemVLM-26B https://huggingface.co/papers/2408.07246
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2024-08-15T00:44:40.000Z
2024-08-17T00:19:30.635Z
[]
/posts/qq8933/310082664847975
1,533
2
960702751544007
[ { "type": "text", "value": "📸Photo LoRA Drop📸", "raw": "📸Photo LoRA Drop📸", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I've been working on this one for a few days, but really I've had this dataset for a few years! I collected a bunch of open access photos online back in late 2022, but I was never happy enough with how they played with the base model!", "raw": "I've been working on this one for a few days, but really I've had this dataset for a few years! I collected a bunch of open access photos online back in late 2022, but I was never happy enough with how they played with the base model!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I am so thrilled that they look so nice with Flux!", "raw": "I am so thrilled that they look so nice with Flux!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This for me is a version one of this model - I still see room for improvement and possibly expansion of it's 40 image dataset. For those who are curious:", "raw": "This for me is a version one of this model - I still see room for improvement and possibly expansion of it's 40 image dataset. For those who are curious:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "40 Image", "raw": "40 Image", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3200 Steps", "raw": "3200 Steps", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dim 32", "raw": "Dim 32", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3e-4", "raw": "3e-4", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Enjoy! Create! Big thank you to Glif for sponsoring the model creation! :D", "raw": "Enjoy! Create! Big thank you to Glif for sponsoring the model creation! :D", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/alvdansen/flux_film_foto", "href": null, "resource": { "type": "model", "id": "alvdansen/flux_film_foto", "discussionNum": null }, "url": "https://huggingface.co/alvdansen/flux_film_foto", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
📸Photo LoRA Drop📸 I've been working on this one for a few days, but really I've had this dataset for a few years! I collected a bunch of open access photos online back in late 2022, but I was never happy enough with how they played with the base model! I am so thrilled that they look so nice with Flux! This for me is a version one of this model - I still see room for improvement and possibly expansion of it's 40 image dataset. For those who are curious: 40 Image 3200 Steps Dim 32 3e-4 Enjoy! Create! Big thank you to Glif for sponsoring the model creation! :D https://huggingface.co/alvdansen/flux_film_foto
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[]
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2024-08-14T21:51:00.000Z
2024-08-14T21:51:00.882Z
[]
/posts/alvdansen/960702751544007
3,157
0
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[ { "type": "text", "value": "AuraSR Giga Upscaler V1 by SECourses - Upscales to 4x", "raw": "AuraSR Giga Upscaler V1 by SECourses - Upscales to 4x", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "AuraSR is a 600M parameter upsampler model derived from the GigaGAN paper. It works super fast and uses a very limited VRAM below 5 GB. It is deterministic upscaler. It works perfect in some images but fails in some images so it is worth to give it a shot.", "raw": "AuraSR is a 600M parameter upsampler model derived from the GigaGAN paper. It works super fast and uses a very limited VRAM below 5 GB. It is deterministic upscaler. It works perfect in some images but fails in some images so it is worth to give it a shot.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "GitHub official repo : ", "raw": "GitHub official repo : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/fal-ai/aura-sr", "href": "https://github.com/fal-ai/aura-sr", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I have developed 1-click installers and a batch upscaler App.", "raw": "I have developed 1-click installers and a batch upscaler App.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can download installers and advanced batch App from below link:", "raw": "You can download installers and advanced batch App from below link:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/110060645", "href": "https://www.patreon.com/posts/110060645", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check the screenshots and examples below", "raw": "Check the screenshots and examples below", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Windows Requirements", "raw": "Windows Requirements", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git", "raw": "Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial", "raw": "If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/-NjNy7afOQ0", "href": "https://youtu.be/-NjNy7afOQ0", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How to Install and Use on Windows", "raw": "How to Install and Use on Windows", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Extract the attached GigaGAN_Upscaler_v1.zip into a folder like c:/giga_upscale", "raw": "Extract the attached GigaGAN_Upscaler_v1.zip into a folder like c:/giga_upscale", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Then double click and install with Windows_Install.bat file", "raw": "Then double click and install with Windows_Install.bat file", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It will generate an isolated virtual environment venv folder and install requirements", "raw": "It will generate an isolated virtual environment venv folder and install requirements", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Then double click and start the Gradio App with Windows_Start_App.bat file", "raw": "Then double click and start the Gradio App with Windows_Start_App.bat file", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "When first time running it will download models into your Hugging Face cache folder", "raw": "When first time running it will download models into your Hugging Face cache folder", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Hugging Face cache folder setup explained below", "raw": "Hugging Face cache folder setup explained below", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/108419878", "href": "https://www.patreon.com/posts/108419878", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All upscaled images will be saved into outputs folder automatically with same name and plus numbering if necessary", "raw": "All upscaled images will be saved into outputs folder automatically with same name and plus numbering if necessary", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can also batch upscale a folder", "raw": "You can also batch upscale a folder", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How to Install and Use on Cloud", "raw": "How to Install and Use on Cloud", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Follow Massed Compute and RunPod instructions", "raw": "Follow Massed Compute and RunPod instructions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Usage is same as on Windows", "raw": "Usage is same as on Windows", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For Kaggle start a Kaggle notebook, import our Kaggle notebook and follow the instructions", "raw": "For Kaggle start a Kaggle notebook, import our Kaggle notebook and follow the instructions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "App Screenshots and Examples below", "raw": "App Screenshots and Examples below", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": 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AuraSR Giga Upscaler V1 by SECourses - Upscales to 4x AuraSR is a 600M parameter upsampler model derived from the GigaGAN paper. It works super fast and uses a very limited VRAM below 5 GB. It is deterministic upscaler. It works perfect in some images but fails in some images so it is worth to give it a shot. GitHub official repo : https://github.com/fal-ai/aura-sr I have developed 1-click installers and a batch upscaler App. You can download installers and advanced batch App from below link: https://www.patreon.com/posts/110060645 Check the screenshots and examples below Windows Requirements Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial https://youtu.be/-NjNy7afOQ0 How to Install and Use on Windows Extract the attached GigaGAN_Upscaler_v1.zip into a folder like c:/giga_upscale Then double click and install with Windows_Install.bat file It will generate an isolated virtual environment venv folder and install requirements Then double click and start the Gradio App with Windows_Start_App.bat file When first time running it will download models into your Hugging Face cache folder Hugging Face cache folder setup explained below https://www.patreon.com/posts/108419878 All upscaled images will be saved into outputs folder automatically with same name and plus numbering if necessary You can also batch upscale a folder How to Install and Use on Cloud Follow Massed Compute and RunPod instructions Usage is same as on Windows For Kaggle start a Kaggle notebook, import our Kaggle notebook and follow the instructions App Screenshots and Examples below
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2024-08-14T21:03:57.000Z
2024-08-14T21:03:57.471Z
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[ { "type": "text", "value": "Introducing Fineweb-Edu-Fortified: An enhanced Fineweb-Edu dataset. 📚", "raw": "Introducing Fineweb-Edu-Fortified: An enhanced Fineweb-Edu dataset. 📚", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This dataset is tailored for NLP tasks and helps streamline model training by offering a more refined, unique dataset. Perfect for startups and researchers looking for high-quality educational content to train, evaluate, or fine-tune AI models. The dataset is based on the Fineweb-Edu subset of the large Fineweb dataset and includes:", "raw": "This dataset is tailored for NLP tasks and helps streamline model training by offering a more refined, unique dataset. Perfect for startups and researchers looking for high-quality educational content to train, evaluate, or fine-tune AI models. The dataset is based on the Fineweb-Edu subset of the large Fineweb dataset and includes:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Exact-match deduplication across all crawls", "raw": "- Exact-match deduplication across all crawls", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Embeddings for each row using the TaylorAI/bge-micro model", "raw": "- Embeddings for each row using the TaylorAI/bge-micro model", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Count column indicating duplication frequency", "raw": "- Count column indicating duplication frequency", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Includes data from 95 Common Crawl crawls (2013-2024)", "raw": "- Includes data from 95 Common Crawl crawls (2013-2024)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Rows have been reduced from 1.279B to 0.324B after deduplication", "raw": "- Rows have been reduced from 1.279B to 0.324B after deduplication", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- It is comprised of ~375B tokens (down from 1,320B in Fineweb-Edu)", "raw": "- It is comprised of ~375B tokens (down from 1,320B in Fineweb-Edu)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Access the entire Fineweb-Edu-Fortified dataset on Hugging Face → ", "raw": "Access the entire Fineweb-Edu-Fortified dataset on Hugging Face → ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/airtrain-ai/fineweb-edu-fortified", "href": null, "resource": { "type": "dataset", "id": "airtrain-ai/fineweb-edu-fortified", "discussionNum": null }, "url": "https://huggingface.co/datasets/airtrain-ai/fineweb-edu-fortified", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Try a semantic search demo via this Hugging Face Space → ", "raw": "Try a semantic search demo via this Hugging Face Space → ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/airtrain-ai/fineweb-edu-fortified-search-demo", "href": null, "resource": { "type": "space", "id": "airtrain-ai/fineweb-edu-fortified-search-demo", "discussionNum": null }, "url": "https://huggingface.co/spaces/airtrain-ai/fineweb-edu-fortified-search-demo", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Many thanks to the amazing ", "raw": "Many thanks to the amazing ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@josh-sematic", "href": null, "resource": null, "url": null, "code": null, "user": "josh-sematic", "label": null, "lang": null }, { "type": "text", "value": " for his work on this project, the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets and for their support during our work on Fineweb-Edu-Fortified, and also thanks to ", "raw": " for his work on this project, the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets and for their support during our work on Fineweb-Edu-Fortified, and also thanks to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@underspirit", "href": null, "resource": null, "url": null, "code": null, "user": "underspirit", "label": null, "lang": null }, { "type": "text", "value": " for pointing out the reduction in dataset size that could be achieved via deduplication. 🤗", "raw": " for pointing out the reduction in dataset size that could be achieved via deduplication. 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Introducing Fineweb-Edu-Fortified: An enhanced Fineweb-Edu dataset. 📚 This dataset is tailored for NLP tasks and helps streamline model training by offering a more refined, unique dataset. Perfect for startups and researchers looking for high-quality educational content to train, evaluate, or fine-tune AI models. The dataset is based on the Fineweb-Edu subset of the large Fineweb dataset and includes: - Exact-match deduplication across all crawls - Embeddings for each row using the TaylorAI/bge-micro model - Count column indicating duplication frequency - Includes data from 95 Common Crawl crawls (2013-2024) - Rows have been reduced from 1.279B to 0.324B after deduplication - It is comprised of ~375B tokens (down from 1,320B in Fineweb-Edu) Access the entire Fineweb-Edu-Fortified dataset on Hugging Face → https://huggingface.co/datasets/airtrain-ai/fineweb-edu-fortified Try a semantic search demo via this Hugging Face Space → https://huggingface.co/spaces/airtrain-ai/fineweb-edu-fortified-search-demo Many thanks to the amazing @josh-sematic for his work on this project, the Fineweb/Fineweb-Edu team at Hugging Face for producing the original datasets and for their support during our work on Fineweb-Edu-Fortified, and also thanks to @underspirit for pointing out the reduction in dataset size that could be achieved via deduplication. 🤗
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2024-08-14T18:45:05.000Z
2024-08-14T18:45:05.366Z
[]
/posts/joylarkin/266261281839963
3,006
0
745512209067729
[ { "type": "text", "value": "Let’s see JEPA in action🤖", "raw": "Let’s see JEPA in action🤖", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Simplified image-based implementation training on a CPU with live preview support - very satisfying to watch:)", "raw": "Simplified image-based implementation training on a CPU with live preview support - very satisfying to watch:)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun.", "raw": "I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of three key components: a context encoder, target encoder and a predictor.", "raw": "At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of three key components: a context encoder, target encoder and a predictor.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb", "href": "https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Let’s see JEPA in action🤖 Simplified image-based implementation training on a CPU with live preview support - very satisfying to watch:) I-JEPA is the image-based version of JEPA (Joint-Embedding Predictive Architecture - an alternative to autoregressive LLM architectures ) pioneered by professor Yann Lecun. At a higher level, I-JEPA predicts image segment representations (Target) based on representations of other segments within the same image (Context). It consists of three key components: a context encoder, target encoder and a predictor. Code: https://github.com/Jaykef/ai-algorithms/blob/main/mnist_ijepa.ipynb
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2024-08-14T13:36:56.000Z
2024-08-15T00:35:12.980Z
[]
/posts/Jaward/745512209067729
1,491
0
179021331327055
[ { "type": "text", "value": "If you are interested in deep reinforcement learning, find my recent survey below:", "raw": "If you are interested in deep reinforcement learning, find my recent survey below:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A Survey Analyzing Generalization in Deep Reinforcement Learning", "raw": "A Survey Analyzing Generalization in Deep Reinforcement Learning", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/pdf/2401.02349", "href": "https://arxiv.org/pdf/2401.02349", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/EzgiKorkmaz/generalization-reinforcement-learning", "href": "https://github.com/EzgiKorkmaz/generalization-reinforcement-learning", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
If you are interested in deep reinforcement learning, find my recent survey below: A Survey Analyzing Generalization in Deep Reinforcement Learning Paper: https://arxiv.org/pdf/2401.02349 GitHub: https://github.com/EzgiKorkmaz/generalization-reinforcement-learning
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2024-08-14T09:27:06.000Z
2024-08-14T09:27:06.576Z
[]
/posts/ezgikorkmaz/179021331327055
1,270
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593632232120320
[ { "type": "text", "value": "It took Google’s Transformer model from 2017 a whopping $900 to train. 💸", "raw": "It took Google’s Transformer model from 2017 a whopping $900 to train. 💸", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! 💰", "raw": "This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! 💰", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). 🤖 ", "raw": "Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). 🤖 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Compared to OpenAI’s GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. 💡", "raw": "Compared to OpenAI’s GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. 💡", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2017: Original Transformer Model: $930 [@Google ] 💻 ", "raw": "2017: Original Transformer Model: $930 [@Google ] 💻 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2018: BERT-Large: $3,288 [@Google] 📚 ", "raw": "2018: BERT-Large: $3,288 [@Google] 📚 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2019: RoBERTa Large: 160k [@Meta] 🌐 ", "raw": "2019: RoBERTa Large: 160k [@Meta] 🌐 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2020: GPT-3(175B): $4.32M [@OpenAI] 🧠 ", "raw": "2020: GPT-3(175B): $4.32M [@OpenAI] 🧠 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2023: Llama 2 70B: $3.93M [@Meta] 🐑 ", "raw": "2023: Llama 2 70B: $3.93M [@Meta] 🐑 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2023: GPT-4: $78.35M [@OpenAI] 🌟 ", "raw": "2023: GPT-4: $78.35M [@OpenAI] 🌟 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now, Gemini Ultra: $191.4M [@Google] 🚀 ", "raw": "Now, Gemini Ultra: $191.4M [@Google] 🚀 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This forms an exponential curve! 🤯", "raw": "This forms an exponential curve! 🤯", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But, why? 🤔 ", "raw": "But, why? 🤔 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Compute, data, and expertise. All three come at a great cost! ⚙️📊💡", "raw": "Compute, data, and expertise. All three come at a great cost! ⚙️📊💡", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Google recently made Gemini-1.5-Flash fine-tuning free, as it's almost impossible for regular businesses to justify an in-house trained foundational model! 🆓", "raw": "Google recently made Gemini-1.5-Flash fine-tuning free, as it's almost impossible for regular businesses to justify an in-house trained foundational model! 🆓", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This barrier of cost is going to result in fewer new foundational ", "raw": "This barrier of cost is going to result in fewer new foundational ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "models/less", "raw": "models/less", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " competition and more fine-tunes! 📉🔄", "raw": " competition and more fine-tunes! 📉🔄", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Data [Stanford University’s 2024 AI Index Report]: ", "raw": "Data [Stanford University’s 2024 AI Index Report]: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://aiindex.stanford.edu/report/", "href": "https://aiindex.stanford.edu/report/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Graphic: ", "raw": "Graphic: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://voronoiapp.com/technology/Googles-Gemini-Ultra-Cost-191M-to-Develop--1088", "href": "https://voronoiapp.com/technology/Googles-Gemini-Ultra-Cost-191M-to-Develop--1088", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Many thanks to everyone spending tons of resources and open-sourcing the models! 🤗", "raw": "Many thanks to everyone spending tons of resources and open-sourcing the models! 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
It took Google’s Transformer model from 2017 a whopping $900 to train. 💸 This in contrast to the $191 million Google spent on Gemini Ultra sounds like a bargain! 💰 Gemini Ultra required 50 billion petaFLOPS (one petaFLOP equals one quadrillion FLOPs). 🤖 Compared to OpenAI’s GPT-4, which required 21 billion petaFLOPS, at a cost of $78 million. 💡 2017: Original Transformer Model: $930 [@Google ] 💻 2018: BERT-Large: $3,288 [@Google] 📚 2019: RoBERTa Large: 160k [@Meta] 🌐 2020: GPT-3(175B): $4.32M [@OpenAI] 🧠 2023: Llama 2 70B: $3.93M [@Meta] 🐑 2023: GPT-4: $78.35M [@OpenAI] 🌟 Now, Gemini Ultra: $191.4M [@Google] 🚀 This forms an exponential curve! 🤯 But, why? 🤔 Compute, data, and expertise. All three come at a great cost! ⚙️📊💡 Google recently made Gemini-1.5-Flash fine-tuning free, as it's almost impossible for regular businesses to justify an in-house trained foundational model! 🆓 This barrier of cost is going to result in fewer new foundational models/less competition and more fine-tunes! 📉🔄 Data [Stanford University’s 2024 AI Index Report]: https://aiindex.stanford.edu/report/ Graphic: https://voronoiapp.com/technology/Googles-Gemini-Ultra-Cost-191M-to-Develop--1088 Many thanks to everyone spending tons of resources and open-sourcing the models! 🤗
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2024-08-14T05:30:34.000Z
2024-08-22T23:12:30.025Z
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@papepipopu hey would you like to join my team me and my partner created a trading bot now we are diving into the machine learning world if you are intrested email me [email protected]
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2024-08-14T02:38:05.000Z
2024-08-15T14:50:52.119Z
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/posts/nebazi12/760983212136091
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[ { "type": "text", "value": "🚨 Code now available for \"Using Large Language Models for Hyperparameter Optimization\" at ", "raw": "🚨 Code now available for \"Using Large Language Models for Hyperparameter Optimization\" at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/michaelrzhang/LLM-HyperOpt", "href": "https://github.com/michaelrzhang/LLM-HyperOpt", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " 🚨", "raw": " 🚨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "TLDR: You can just ask LLMs which hyperparameters to use, and it works pretty well! You can even directly optimize your model’s code as a hyperparameter with this.", "raw": "TLDR: You can just ask LLMs which hyperparameters to use, and it works pretty well! You can even directly optimize your model’s code as a hyperparameter with this.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out the paper at ", "raw": "Check out the paper at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2312.04528", "href": "https://arxiv.org/abs/2312.04528", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - with Michael Zhang, Nishkrit Desai, Juhan Bae, and Jimmy Ba", "raw": " - with Michael Zhang, Nishkrit Desai, Juhan Bae, and Jimmy Ba", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
🚨 Code now available for "Using Large Language Models for Hyperparameter Optimization" at https://github.com/michaelrzhang/LLM-HyperOpt 🚨 TLDR: You can just ask LLMs which hyperparameters to use, and it works pretty well! You can even directly optimize your model’s code as a hyperparameter with this. Check out the paper at https://arxiv.org/abs/2312.04528 - with Michael Zhang, Nishkrit Desai, Juhan Bae, and Jimmy Ba
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2024-08-14T02:27:31.000Z
2024-08-14T02:27:31.666Z
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/posts/lorraine2/835007687119871
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booooring
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2024-08-14T01:05:08.000Z
2024-08-14T20:56:34.535Z
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[ { "type": "text", "value": "Most LLMs are not reproducible because the underlying deep neural networks are not. Because that's something LLM creators don't care about. We do, and ours are reproducible, including our GenAI that uses GAN. ", "raw": "Most LLMs are not reproducible because the underlying deep neural networks are not. Because that's something LLM creators don't care about. We do, and ours are reproducible, including our GenAI that uses GAN. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All you have to do is allow the user to specify the seeds of the random number generators involved. First, you need a good random generator you have full control over. Better than numpy.random. See ours, with infinite period and one line of code, faster and better than what's in Python and elsewhere. Here is the link: ", "raw": "All you have to do is allow the user to specify the seeds of the random number generators involved. First, you need a good random generator you have full control over. Better than numpy.random. See ours, with infinite period and one line of code, faster and better than what's in Python and elsewhere. Here is the link: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mltblog.com/4fGDLu0", "href": "https://mltblog.com/4fGDLu0", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Most LLMs are not reproducible because the underlying deep neural networks are not. Because that's something LLM creators don't care about. We do, and ours are reproducible, including our GenAI that uses GAN. All you have to do is allow the user to specify the seeds of the random number generators involved. First, you need a good random generator you have full control over. Better than numpy.random. See ours, with infinite period and one line of code, faster and better than what's in Python and elsewhere. Here is the link: https://mltblog.com/4fGDLu0
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2024-08-14T00:18:43.000Z
2024-08-14T22:56:04.737Z
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Chomsky predicting LLMs in 1956, curated by Ryan Rhodes (Rutgers)
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2024-08-13T17:06:42.000Z
2024-08-13T17:06:42.666Z
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mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq 99% of the performance across various benchmarks! https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq
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2024-08-12T18:56:29.000Z
2024-08-12T18:56:29.759Z
[]
/posts/KnutJaegersberg/925847991625087
2,310
0
681836693682285
[ { "type": "text", "value": "I'm excited to announce that Transformers.js V3 is finally available on NPM! 🔥 State-of-the-art Machine Learning for the web, now with WebGPU support! 🤯⚡️", "raw": "I'm excited to announce that Transformers.js V3 is finally available on NPM! 🔥 State-of-the-art Machine Learning for the web, now with WebGPU support! 🤯⚡️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Install it from NPM with:", "raw": "Install it from NPM with:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "𝚗𝚙𝚖 𝚒 @𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎/𝚝𝚛𝚊𝚗𝚜𝚏𝚘𝚛𝚖𝚎𝚛𝚜", "raw": "𝚗𝚙𝚖 𝚒 @𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎/𝚝𝚛𝚊𝚗𝚜𝚏𝚘𝚛𝚖𝚎𝚛𝚜", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "or via CDN, for example: ", "raw": "or via CDN, for example: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://v2.scrimba.com/s0lmm0qh1q", "href": "https://v2.scrimba.com/s0lmm0qh1q", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Segment Anything demo: ", "raw": "Segment Anything demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/webml-community/segment-anything-webgpu", "href": null, "resource": { "type": "space", "id": "webml-community/segment-anything-webgpu", "discussionNum": null }, "url": "https://huggingface.co/spaces/webml-community/segment-anything-webgpu", "code": null, "user": null, "label": null, "lang": null } ]
I'm excited to announce that Transformers.js V3 is finally available on NPM! 🔥 State-of-the-art Machine Learning for the web, now with WebGPU support! 🤯⚡️ Install it from NPM with: 𝚗𝚙𝚖 𝚒 @𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎/𝚝𝚛𝚊𝚗𝚜𝚏𝚘𝚛𝚖𝚎𝚛𝚜 or via CDN, for example: https://v2.scrimba.com/s0lmm0qh1q Segment Anything demo: https://huggingface.co/spaces/webml-community/segment-anything-webgpu
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2024-08-12T16:36:51.000Z
2024-10-24T19:08:59.184Z
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/posts/Xenova/681836693682285
14,876
5
159368967552315
[ { "type": "text", "value": "Hey HF. I just released a new reward modelling dataset: ", "raw": "Hey HF. I just released a new reward modelling dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Avelina/UltraSteer-v0", "href": null, "resource": { "type": "dataset", "id": "Avelina/UltraSteer-v0", "discussionNum": null }, "url": "https://huggingface.co/datasets/Avelina/UltraSteer-v0", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's ", "raw": "UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM", "href": null, "resource": { "type": "model", "id": "nvidia/Llama2-13B-SteerLM-RM", "discussionNum": null }, "url": "https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.", "raw": " reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.", "raw": "This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.", "raw": "I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hey HF. I just released a new reward modelling dataset: https://huggingface.co/datasets/Avelina/UltraSteer-v0 UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model. This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper. I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.
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2024-08-12T15:29:21.000Z
2024-08-17T13:23:13.617Z
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/posts/Avelina/159368967552315
2,164
2
285509068969829
[ { "type": "text", "value": "Releasing HQQ Llama-3.1-70b 4-bit quantized version! Check it out at ", "raw": "Releasing HQQ Llama-3.1-70b 4-bit quantized version! Check it out at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq", "href": null, "resource": { "type": "model", "id": "mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq", "discussionNum": null }, "url": "https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". ", "raw": ". ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Achieves 99% of the base model performance across various benchmarks! Details in the model card. ", "raw": "Achieves 99% of the base model performance across various benchmarks! Details in the model card. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Releasing HQQ Llama-3.1-70b 4-bit quantized version! Check it out at https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq. Achieves 99% of the base model performance across various benchmarks! Details in the model card.
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2024-08-12T14:48:59.000Z
2024-08-12T14:48:59.725Z
[]
/posts/appoose/285509068969829
2,056
0
884103958631691
[ { "type": "text", "value": "FalconMamba 7B - a new model from TII (Technology Innovation Institute) is out !", "raw": "FalconMamba 7B - a new model from TII (Technology Innovation Institute) is out !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Blogpost: ", "raw": "- Blogpost: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/falconmamba", "href": "https://huggingface.co/blog/falconmamba", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Link to collection: ", "raw": "- Link to collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a", "href": null, "resource": { "type": "collection", "id": "tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a", "discussionNum": null }, "url": "https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Link to playground: ", "raw": "- Link to playground: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/tiiuae/falcon-mamba-playground", "href": null, "resource": { "type": "space", "id": "tiiuae/falcon-mamba-playground", "discussionNum": null }, "url": "https://huggingface.co/spaces/tiiuae/falcon-mamba-playground", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
FalconMamba 7B - a new model from TII (Technology Innovation Institute) is out ! - Blogpost: https://huggingface.co/blog/falconmamba - Link to collection: https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a - Link to playground: https://huggingface.co/spaces/tiiuae/falcon-mamba-playground
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2024-08-12T14:26:48.000Z
2024-08-12T14:26:48.320Z
[]
/posts/ybelkada/884103958631691
3,394
0
389817265017183
[ { "type": "text", "value": "If you are interested in Knowledge Graphs, I invented all of this a year ago. It is a Encoder/Decoder that works with Knowledge Graphs. I am glad the world finally realizes this is useful a year later. I tried to tell you. I have not licensed any of the math. I own all of it. I do not have any plans to ever enforce the licensing but I like holding onto it. ", "raw": "If you are interested in Knowledge Graphs, I invented all of this a year ago. It is a Encoder/Decoder that works with Knowledge Graphs. I am glad the world finally realizes this is useful a year later. I tried to tell you. I have not licensed any of the math. I own all of it. I do not have any plans to ever enforce the licensing but I like holding onto it. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/TuringsSolutions/pfafresearch", "href": "https://huggingface.co/blog/TuringsSolutions/pfafresearch", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
If you are interested in Knowledge Graphs, I invented all of this a year ago. It is a Encoder/Decoder that works with Knowledge Graphs. I am glad the world finally realizes this is useful a year later. I tried to tell you. I have not licensed any of the math. I own all of it. I do not have any plans to ever enforce the licensing but I like holding onto it. https://huggingface.co/blog/TuringsSolutions/pfafresearch
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2024-08-12T12:21:13.000Z
2024-08-16T15:00:22.544Z
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/posts/TuringsSolutions/389817265017183
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[ { "type": "text", "value": "Here is an AI Puzzle!", "raw": "Here is an AI Puzzle!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "When you solve it just use a 😎 emoji.", "raw": "When you solve it just use a 😎 emoji.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "NO SPOILERS", "raw": "NO SPOILERS", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A similar puzzle might have each picture that has a hidden meaning of summer, winter, fall, spring, and the answer would be seasons.", "raw": "A similar puzzle might have each picture that has a hidden meaning of summer, winter, fall, spring, and the answer would be seasons.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Its a little dated now (almost a year), so bottom right might be tough. ", "raw": "Its a little dated now (almost a year), so bottom right might be tough. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thanks to ", "raw": "Thanks to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@johko", "href": null, "resource": null, "url": null, "code": null, "user": "johko", "label": null, "lang": null }, { "type": "text", "value": " for the encouragement to post!", "raw": " for the encouragement to post!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Here is an AI Puzzle! When you solve it just use a 😎 emoji. NO SPOILERS A similar puzzle might have each picture that has a hidden meaning of summer, winter, fall, spring, and the answer would be seasons. Its a little dated now (almost a year), so bottom right might be tough. Thanks to @johko for the encouragement to post!
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2024-08-12T09:30:10.000Z
2024-08-12T09:36:52.466Z
[]
/posts/derek-thomas/790598307520288
2,053
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How can i check my resources limitation and usage?
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2024-08-12T08:36:38.000Z
2024-08-13T21:53:27.335Z
[]
/posts/measmonysuon/763181990625291
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[ { "type": "text", "value": "SFT + Quantisation + Unsloth is a super easy way of squeezing extra performance out of an LLM at low latencies. Here are some hand y resources to bootstrap your projects.", "raw": "SFT + Quantisation + Unsloth is a super easy way of squeezing extra performance out of an LLM at low latencies. Here are some hand y resources to bootstrap your projects.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here's a filtered dataset from Helpsteer2 with the most correct and coherent samples: ", "raw": "Here's a filtered dataset from Helpsteer2 with the most correct and coherent samples: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/burtenshaw/helpsteer-2-plus", "href": null, "resource": { "type": "dataset", "id": "burtenshaw/helpsteer-2-plus", "discussionNum": null }, "url": "https://huggingface.co/datasets/burtenshaw/helpsteer-2-plus", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is a SFT finetuned model: ttps://huggingface.co/burtenshaw/gemma-help-tiny-sft", "raw": "This is a SFT finetuned model: ttps://huggingface.co/burtenshaw/gemma-help-tiny-sft", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is the notebook I use to train the model: ", "raw": "This is the notebook I use to train the model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/17oskw_5lil5C3jCW34rA-EXjXnGgRRZw?usp=sharing", "href": "https://colab.research.google.com/drive/17oskw_5lil5C3jCW34rA-EXjXnGgRRZw?usp=sharing", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here's a load of Unsloth notebook on finetuning and inference: ", "raw": "Here's a load of Unsloth notebook on finetuning and inference: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://docs.unsloth.ai/get-started/unsloth-notebooks", "href": "https://docs.unsloth.ai/get-started/unsloth-notebooks", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
SFT + Quantisation + Unsloth is a super easy way of squeezing extra performance out of an LLM at low latencies. Here are some hand y resources to bootstrap your projects. Here's a filtered dataset from Helpsteer2 with the most correct and coherent samples: https://huggingface.co/datasets/burtenshaw/helpsteer-2-plus This is a SFT finetuned model: ttps://huggingface.co/burtenshaw/gemma-help-tiny-sft This is the notebook I use to train the model: https://colab.research.google.com/drive/17oskw_5lil5C3jCW34rA-EXjXnGgRRZw?usp=sharing Here's a load of Unsloth notebook on finetuning and inference: https://docs.unsloth.ai/get-started/unsloth-notebooks
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2024-08-12T08:27:20.000Z
2024-08-12T08:27:20.492Z
[]
/posts/burtenshaw/897737525699797
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235312600642861
[ { "type": "text", "value": "🚀 RAGoon is now available on PyPI, GitHub, and as a Space on Hugging Face for batched embeddings generation 🤗", "raw": "🚀 RAGoon is now available on PyPI, GitHub, and as a Space on Hugging Face for batched embeddings generation 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "RAGoon is a set of NLP utilities for multi-model embedding production, high-dimensional vector visualization, and aims to improve language model performance by providing contextually relevant information through search-based querying, web scraping and data augmentation techniques.", "raw": "RAGoon is a set of NLP utilities for multi-model embedding production, high-dimensional vector visualization, and aims to improve language model performance by providing contextually relevant information through search-based querying, web scraping and data augmentation techniques.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "At this stage, 5 major classes are available via RAGoon to facilitate:", "raw": "At this stage, 5 major classes are available via RAGoon to facilitate:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- the production of chain embeddings for several models to simplify a continuous deployment process;", "raw": "- the production of chain embeddings for several models to simplify a continuous deployment process;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- production of LLM requests for web querying and content retrieval via the Google API;", "raw": "- production of LLM requests for web querying and content retrieval via the Google API;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- recursive chunking via tokens;", "raw": "- recursive chunking via tokens;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- data visualization and the function to load embeddings from a FAISS index, reduce their dimensionality using PCA and/or t-SNE, and visualize them in an interactive 3D graph;", "raw": "- data visualization and the function to load embeddings from a FAISS index, reduce their dimensionality using PCA and/or t-SNE, and visualize them in an interactive 3D graph;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- the creation of binary indexes for search with scalar (int8) rescoring. ", "raw": "- the creation of binary indexes for search with scalar (int8) rescoring. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link to GitHub: ", "raw": "Link to GitHub: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/louisbrulenaudet/ragoon", "href": "https://github.com/louisbrulenaudet/ragoon", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Link to the 🤗 Space: ", "raw": "Link to the 🤗 Space: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/louisbrulenaudet/ragoon", "href": null, "resource": { "type": "space", "id": "louisbrulenaudet/ragoon", "discussionNum": null }, "url": "https://huggingface.co/spaces/louisbrulenaudet/ragoon", "code": null, "user": null, "label": null, "lang": null } ]
🚀 RAGoon is now available on PyPI, GitHub, and as a Space on Hugging Face for batched embeddings generation 🤗 RAGoon is a set of NLP utilities for multi-model embedding production, high-dimensional vector visualization, and aims to improve language model performance by providing contextually relevant information through search-based querying, web scraping and data augmentation techniques. At this stage, 5 major classes are available via RAGoon to facilitate: - the production of chain embeddings for several models to simplify a continuous deployment process; - production of LLM requests for web querying and content retrieval via the Google API; - recursive chunking via tokens; - data visualization and the function to load embeddings from a FAISS index, reduce their dimensionality using PCA and/or t-SNE, and visualize them in an interactive 3D graph; - the creation of binary indexes for search with scalar (int8) rescoring. Link to GitHub: https://github.com/louisbrulenaudet/ragoon Link to the 🤗 Space: https://huggingface.co/spaces/louisbrulenaudet/ragoon
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2024-08-12T05:37:46.000Z
2024-08-12T05:37:46.760Z
[]
/posts/louisbrulenaudet/235312600642861
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[ { "type": "text", "value": "BiRefNet State Of The Art Newest Very Best Background Batch Remover APP", "raw": "BiRefNet State Of The Art Newest Very Best Background Batch Remover APP", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Official repo : ", "raw": "Official repo : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/ZhengPeng7/BiRefNet", "href": "https://github.com/ZhengPeng7/BiRefNet", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Download APP and installers from : ", "raw": "Download APP and installers from : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/109913645", "href": "https://www.patreon.com/posts/109913645", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Hugging Face Demo : ", "raw": "Hugging Face Demo : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo", "href": null, "resource": { "type": "space", "id": "ZhengPeng7/BiRefNet_demo", "discussionNum": null }, "url": "https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I have developed a very advanced Gradio APP for this with full proper file saving and batch processing. Also my version removes BG and saves as transparent background.", "raw": "I have developed a very advanced Gradio APP for this with full proper file saving and batch processing. Also my version removes BG and saves as transparent background.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The APP uses huge VRAM for high resolution images. However it is still working uber fast even though using shared VRAM. So make sure that you have high RAM or set virtual RAM.", "raw": "The APP uses huge VRAM for high resolution images. However it is still working uber fast even though using shared VRAM. So make sure that you have high RAM or set virtual RAM.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Click below to see how to set virtual RAM on Windows.", "raw": "Click below to see how to set virtual RAM on Windows.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.windowscentral.com/how-change-virtual-memory-size-windows-10", "href": "https://www.windowscentral.com/how-change-virtual-memory-size-windows-10", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "On Massed Compute A6000 GPU (31 cents per hour) you can very fast remove even very high res images backgrounds.", "raw": "On Massed Compute A6000 GPU (31 cents per hour) you can very fast remove even very high res images backgrounds.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Currently we have 1 click installers for RunPod, Massed Compute, Kaggle and Windows.", "raw": "Currently we have 1 click installers for RunPod, Massed Compute, Kaggle and Windows.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Windows Requirements", "raw": "Windows Requirements", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git", "raw": "Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial", "raw": "If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/-NjNy7afOQ0", "href": "https://youtu.be/-NjNy7afOQ0", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How To Use On Windows", "raw": "How To Use On Windows", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Just extract files into like c:/BiRefNet_v1", "raw": "Just extract files into like c:/BiRefNet_v1", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Double click Windows_Install.bat file and it will generate a isolated virtual environment and install requirements", "raw": "Double click Windows_Install.bat file and it will generate a isolated virtual environment and install requirements", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It will automatically download models into your Hugging Face cache (best model under 1 GB)", "raw": "It will automatically download models into your Hugging Face cache (best model under 1 GB)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Then start and use the Gradio APP with Windows_Start_App.bat", "raw": "Then start and use the Gradio APP with Windows_Start_App.bat", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Cloud How To Use", "raw": "Cloud How To Use", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Massed Compute, RunPod has instructions txt files. Follow them", "raw": "Massed Compute, RunPod has instructions txt files. Follow them", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Kaggle has all the instructions 1 by 1", "raw": "Kaggle has all the instructions 1 by 1", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "On Kaggle set resolution 1024x1024 or you will get out of memory error", "raw": "On Kaggle set resolution 1024x1024 or you will get out of memory error", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
BiRefNet State Of The Art Newest Very Best Background Batch Remover APP Official repo : https://github.com/ZhengPeng7/BiRefNet Download APP and installers from : https://www.patreon.com/posts/109913645 Hugging Face Demo : https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo I have developed a very advanced Gradio APP for this with full proper file saving and batch processing. Also my version removes BG and saves as transparent background. The APP uses huge VRAM for high resolution images. However it is still working uber fast even though using shared VRAM. So make sure that you have high RAM or set virtual RAM. Click below to see how to set virtual RAM on Windows. https://www.windowscentral.com/how-change-virtual-memory-size-windows-10 On Massed Compute A6000 GPU (31 cents per hour) you can very fast remove even very high res images backgrounds. Currently we have 1 click installers for RunPod, Massed Compute, Kaggle and Windows. Windows Requirements Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial https://youtu.be/-NjNy7afOQ0 How To Use On Windows Just extract files into like c:/BiRefNet_v1 Double click Windows_Install.bat file and it will generate a isolated virtual environment and install requirements It will automatically download models into your Hugging Face cache (best model under 1 GB) Then start and use the Gradio APP with Windows_Start_App.bat Cloud How To Use Massed Compute, RunPod has instructions txt files. Follow them Kaggle has all the instructions 1 by 1 On Kaggle set resolution 1024x1024 or you will get out of memory error
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2024-08-11T22:39:42.000Z
2024-08-11T22:39:42.358Z
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[ { "type": "text", "value": "AutoGen from ", "raw": "AutoGen from ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Microsoft", "href": null, "resource": null, "url": null, "code": null, "user": "Microsoft", "label": null, "lang": null }, { "type": "text", "value": " is crazy! 🚀 It's an open-source framework that allows LLM agents to chat with each other to solve your tasks. 🤖💬", "raw": " is crazy! 🚀 It's an open-source framework that allows LLM agents to chat with each other to solve your tasks. 🤖💬", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They use the Assistant-Agent and User-Proxy-Agent framework! 🛠️", "raw": "They use the Assistant-Agent and User-Proxy-Agent framework! 🛠️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "As the name suggests, the Assistant-Agent does the work, and the User-Proxy-Agent behaves like a human, guiding the Assistant-Agent and double-checking its work! 🧑‍💻✅", "raw": "As the name suggests, the Assistant-Agent does the work, and the User-Proxy-Agent behaves like a human, guiding the Assistant-Agent and double-checking its work! 🧑‍💻✅", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Both Assistant-Agent and User-Proxy-Agent can be the same or different LLMs. 🤔🔄", "raw": "Both Assistant-Agent and User-Proxy-Agent can be the same or different LLMs. 🤔🔄", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. 🌟", "raw": "AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. 🌟", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is truly amazing for building agentic AI quickly! 🚀✨", "raw": "This is truly amazing for building agentic AI quickly! 🚀✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/microsoft/autogen", "href": "https://github.com/microsoft/autogen", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " 🔗", "raw": " 🔗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```Python\nfrom autogen import AssistantAgent, UserProxyAgent, config_list_from_json\n\n#config\nconfig_list = config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n\nassistant = AssistantAgent(\"assistant\", llm_config={\"config_list\": config_list})\nuser_proxy = UserProxyAgent(\"user_proxy\", code_execution_config={\"work_dir\": \"coding\", \"use_docker\": False}) \n\nuser_proxy.initiate_chat(assistant, message=\"Plot a chart of NVDA and TESLA stock price change YTD.\")\n# This initiates an automated chat between the two agents to solve the task\n```", "href": null, "resource": null, "url": null, "code": "from autogen import AssistantAgent, UserProxyAgent, config_list_from_json\n\n#config\nconfig_list = config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n\nassistant = AssistantAgent(\"assistant\", llm_config={\"config_list\": config_list})\nuser_proxy = UserProxyAgent(\"user_proxy\", code_execution_config={\"work_dir\": \"coding\", \"use_docker\": False}) \n\nuser_proxy.initiate_chat(assistant, message=\"Plot a chart of NVDA and TESLA stock price change YTD.\")\n# This initiates an automated chat between the two agents to solve the task", "user": null, "label": null, "lang": "Python" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
AutoGen from @Microsoft is crazy! 🚀 It's an open-source framework that allows LLM agents to chat with each other to solve your tasks. 🤖💬 They use the Assistant-Agent and User-Proxy-Agent framework! 🛠️ As the name suggests, the Assistant-Agent does the work, and the User-Proxy-Agent behaves like a human, guiding the Assistant-Agent and double-checking its work! 🧑‍💻✅ Both Assistant-Agent and User-Proxy-Agent can be the same or different LLMs. 🤔🔄 AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. 🌟 This is truly amazing for building agentic AI quickly! 🚀✨ GitHub: https://github.com/microsoft/autogen 🔗 ```Python from autogen import AssistantAgent, UserProxyAgent, config_list_from_json #config config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST") assistant = AssistantAgent("assistant", llm_config={"config_list": config_list}) user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding", "use_docker": False}) user_proxy.initiate_chat(assistant, message="Plot a chart of NVDA and TESLA stock price change YTD.") # This initiates an automated chat between the two agents to solve the task ```
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2024-08-11T17:56:28.000Z
2024-08-11T17:56:28.612Z
[]
/posts/singhsidhukuldeep/790332044897556
2,146
0
908022150407954
[ { "type": "text", "value": "LLaVA-o1 🔥 NEW visual language model with spontaneous and systematic reasoning, like GPT-o1! ", "raw": "LLaVA-o1 🔥 NEW visual language model with spontaneous and systematic reasoning, like GPT-o1! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2411.10440", "href": null, "resource": { "type": "paper", "id": "2411.10440", "discussionNum": null }, "url": "https://huggingface.co/papers/2411.10440", "code": null, "user": null, "label": "LLaVA-o1: Let Vision Language Models Reason Step-by-Step (2411.10440)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Github: ", "raw": "Github: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/PKU-YuanGroup/LLaVA-o1", "href": "https://github.com/PKU-YuanGroup/LLaVA-o1", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "✨ Autonomous Multistage Reasoning", "raw": "✨ Autonomous Multistage Reasoning", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "✨ Efficient with Small Data: Trained on 100k samples ", "raw": "✨ Efficient with Small Data: Trained on 100k samples ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "✨ Innovative Inference: Stepwise beam search boosts precision & scalability in reasoning.", "raw": "✨ Innovative Inference: Stepwise beam search boosts precision & scalability in reasoning.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
LLaVA-o1 🔥 NEW visual language model with spontaneous and systematic reasoning, like GPT-o1! Paper: https://huggingface.co/papers/2411.10440 Github: https://github.com/PKU-YuanGroup/LLaVA-o1 ✨ Autonomous Multistage Reasoning ✨ Efficient with Small Data: Trained on 100k samples ✨ Innovative Inference: Stepwise beam search boosts precision & scalability in reasoning.
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2024-11-18T17:07:50.000Z
2024-11-18T21:44:52.018Z
[]
/posts/AdinaY/908022150407954
373
0
840936591127590
[ { "type": "text", "value": "🖼️ Introducing Public Domain Pictures Dataset - ", "raw": "🖼️ Introducing Public Domain Pictures Dataset - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/nyuuzyou/publicdomainpictures", "href": null, "resource": { "type": "dataset", "id": "nyuuzyou/publicdomainpictures", "discussionNum": null }, "url": "https://huggingface.co/datasets/nyuuzyou/publicdomainpictures", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dataset highlights:", "raw": "Dataset highlights:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- 644,412 public domain images with comprehensive metadata from publicdomainpictures.net", "raw": "- 644,412 public domain images with comprehensive metadata from publicdomainpictures.net", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- English language metadata including titles, descriptions, and keywords", "raw": "- English language metadata including titles, descriptions, and keywords", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Each entry contains rich metadata including:", "raw": "- Each entry contains rich metadata including:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - Unique image ID and full-size image URLs", "raw": " - Unique image ID and full-size image URLs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - Detailed titles and descriptions", "raw": " - Detailed titles and descriptions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - Keyword/tag collections", "raw": " - Keyword/tag collections", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - Creator attribution", "raw": " - Creator attribution", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Released to the public domain under Creative Commons Zero (CC0) license", "raw": "- Released to the public domain under Creative Commons Zero (CC0) license", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
🖼️ Introducing Public Domain Pictures Dataset - https://huggingface.co/datasets/nyuuzyou/publicdomainpictures Dataset highlights: - 644,412 public domain images with comprehensive metadata from publicdomainpictures.net - English language metadata including titles, descriptions, and keywords - Each entry contains rich metadata including: - Unique image ID and full-size image URLs - Detailed titles and descriptions - Keyword/tag collections - Creator attribution - Released to the public domain under Creative Commons Zero (CC0) license
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2024-11-18T17:06:16.000Z
2024-11-19T21:29:01.485Z
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/posts/nyuuzyou/840936591127590
919
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403752390935785
[ { "type": "text", "value": "You can clean and format datasets entirely in the browser with a few lines of SQL. ", "raw": "You can clean and format datasets entirely in the browser with a few lines of SQL. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In this post, I replicate the process ", "raw": "In this post, I replicate the process ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@mlabonne", "href": null, "resource": null, "url": null, "code": null, "user": "mlabonne", "label": null, "lang": null }, { "type": "text", "value": " used to clean the new ", "raw": " used to clean the new ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1", "href": null, "resource": { "type": "dataset", "id": "microsoft/orca-agentinstruct-1M-v1", "discussionNum": null }, "url": "https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " dataset. ", "raw": " dataset. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The cleaning process consists of:", "raw": "The cleaning process consists of:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Joining the separate splits together / add split column", "raw": "- Joining the separate splits together / add split column", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Converting string messages into list of structs", "raw": "- Converting string messages into list of structs", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Removing empty system prompts", "raw": "- Removing empty system prompts", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/cfahlgren1/the-beginners-guide-to-cleaning-a-dataset", "href": "https://huggingface.co/blog/cfahlgren1/the-beginners-guide-to-cleaning-a-dataset", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here's his new cleaned dataset: ", "raw": "Here's his new cleaned dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/mlabonne/orca-agentinstruct-1M-v1-cleaned", "href": null, "resource": { "type": "dataset", "id": "mlabonne/orca-agentinstruct-1M-v1-cleaned", "discussionNum": null }, "url": "https://huggingface.co/datasets/mlabonne/orca-agentinstruct-1M-v1-cleaned", "code": null, "user": null, "label": null, "lang": null } ]
You can clean and format datasets entirely in the browser with a few lines of SQL. In this post, I replicate the process @mlabonne used to clean the new https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1 dataset. The cleaning process consists of: - Joining the separate splits together / add split column - Converting string messages into list of structs - Removing empty system prompts https://huggingface.co/blog/cfahlgren1/the-beginners-guide-to-cleaning-a-dataset Here's his new cleaned dataset: https://huggingface.co/datasets/mlabonne/orca-agentinstruct-1M-v1-cleaned
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[]
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2024-11-18T16:17:40.000Z
2024-11-20T13:36:44.651Z
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/posts/cfahlgren1/403752390935785
2,845
1
158425220752419
[ { "type": "text", "value": "Some time ago, I built a predictive LLM router that routes chat requests between small and large LLM models based on prompt classification. It dynamically selects the most suitable model depending on the complexity of the user input, ensuring optimal performance while maintaining conversation context. I also fine-tuned a RoBERTa model to use with the package, but you can plug and play any classifier of your choice.", "raw": "Some time ago, I built a predictive LLM router that routes chat requests between small and large LLM models based on prompt classification. It dynamically selects the most suitable model depending on the complexity of the user input, ensuring optimal performance while maintaining conversation context. I also fine-tuned a RoBERTa model to use with the package, but you can plug and play any classifier of your choice.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Project's homepage:", "raw": "Project's homepage:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://devquasar.com/llm-predictive-router/", "href": "https://devquasar.com/llm-predictive-router/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Pypi:", "raw": "Pypi:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://pypi.org/project/llm-predictive-router/", "href": "https://pypi.org/project/llm-predictive-router/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model:", "raw": "Model:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/DevQuasar/roberta-prompt_classifier-v0.1", "href": null, "resource": { "type": "model", "id": "DevQuasar/roberta-prompt_classifier-v0.1", "discussionNum": null }, "url": "https://huggingface.co/DevQuasar/roberta-prompt_classifier-v0.1", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Training data:", "raw": "Training data:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/DevQuasar/llm_router_dataset-synth", "href": null, "resource": { "type": "dataset", "id": "DevQuasar/llm_router_dataset-synth", "discussionNum": null }, "url": "https://huggingface.co/datasets/DevQuasar/llm_router_dataset-synth", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Git:", "raw": "Git:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/csabakecskemeti/llm_predictive_router_package", "href": "https://github.com/csabakecskemeti/llm_predictive_router_package", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Feel free to check it out, and/or contribute.", "raw": "Feel free to check it out, and/or contribute.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Some time ago, I built a predictive LLM router that routes chat requests between small and large LLM models based on prompt classification. It dynamically selects the most suitable model depending on the complexity of the user input, ensuring optimal performance while maintaining conversation context. I also fine-tuned a RoBERTa model to use with the package, but you can plug and play any classifier of your choice. Project's homepage: https://devquasar.com/llm-predictive-router/ Pypi: https://pypi.org/project/llm-predictive-router/ Model: https://huggingface.co/DevQuasar/roberta-prompt_classifier-v0.1 Training data: https://huggingface.co/datasets/DevQuasar/llm_router_dataset-synth Git: https://github.com/csabakecskemeti/llm_predictive_router_package Feel free to check it out, and/or contribute.
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2024-11-18T15:55:40.000Z
2024-11-18T15:55:55.915Z
[]
/posts/csabakecskemeti/158425220752419
1,201
0
480854987588678
[ { "type": "text", "value": "🤗 transformers pipelines now support vision language models for easy local inference 🫰🏻 ", "raw": "🤗 transformers pipelines now support vision language models for easy local inference 🫰🏻 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "h/t ", "raw": "h/t ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@yonigozlan", "href": null, "resource": null, "url": null, "code": null, "user": "yonigozlan", "label": null, "lang": null }, { "type": "text", "value": " for shipping this 🎩👏", "raw": " for shipping this 🎩👏", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "you can also use inference API to infer hosted vision LMs (via Python, JS and cURL) ", "raw": "you can also use inference API to infer hosted vision LMs (via Python, JS and cURL) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/api-inference/en/tasks/image-text-to-text", "href": "https://huggingface.co/docs/api-inference/en/tasks/image-text-to-text", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
🤗 transformers pipelines now support vision language models for easy local inference 🫰🏻 h/t @yonigozlan for shipping this 🎩👏 you can also use inference API to infer hosted vision LMs (via Python, JS and cURL) https://huggingface.co/docs/api-inference/en/tasks/image-text-to-text
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2024-11-18T15:51:21.000Z
2024-11-18T15:51:21.974Z
[]
/posts/merve/480854987588678
1,722
0
740213936969263
[ { "type": "text", "value": "Build a fine-tuning dataset with No Code.", "raw": "Build a fine-tuning dataset with No Code.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Do you want to build a small dataset for creative writing to fine-tune an Open LLM?", "raw": "Do you want to build a small dataset for creative writing to fine-tune an Open LLM?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Find a dataset full of conversations with ChatGPT on the Hugging Face Hub.", "raw": "- Find a dataset full of conversations with ChatGPT on the Hugging Face Hub.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Import it into your Argilla Space.", "raw": "- Import it into your Argilla Space.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Preview the dataset and create a question to label the relevant conversations.", "raw": "- Preview the dataset and create a question to label the relevant conversations.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Label 1000 valid examples of creating writing.", "raw": "- Label 1000 valid examples of creating writing.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Use this dataset with Autotrain to fine-tune your model.", "raw": "- Use this dataset with Autotrain to fine-tune your model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Build a fine-tuning dataset with No Code. Do you want to build a small dataset for creative writing to fine-tune an Open LLM? - Find a dataset full of conversations with ChatGPT on the Hugging Face Hub. - Import it into your Argilla Space. - Preview the dataset and create a question to label the relevant conversations. - Label 1000 valid examples of creating writing. - Use this dataset with Autotrain to fine-tune your model.
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[]
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2024-11-18T14:44:02.000Z
2024-11-18T23:36:16.212Z
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/posts/Ameeeee/740213936969263
1,198
1
826060405634197
[ { "type": "text", "value": "For anyone who struggles with NER or information extraction with LLM.", "raw": "For anyone who struggles with NER or information extraction with LLM.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We showed an efficient workflow for token classification including zero-shot suggestions and model fine-tuning with Argilla, GliNER, the NuMind NuExtract LLM and SpanMarker. ", "raw": "We showed an efficient workflow for token classification including zero-shot suggestions and model fine-tuning with Argilla, GliNER, the NuMind NuExtract LLM and SpanMarker. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@argilla", "href": null, "resource": null, "url": null, "code": null, "user": "argilla", "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Video: ", "raw": "Video: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/JvLpaYgNd84?feature=shared", "href": "https://youtu.be/JvLpaYgNd84?feature=shared", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Notebooks and slides included to try it yourself 🙂 ", "raw": "Notebooks and slides included to try it yourself 🙂 ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
For anyone who struggles with NER or information extraction with LLM. We showed an efficient workflow for token classification including zero-shot suggestions and model fine-tuning with Argilla, GliNER, the NuMind NuExtract LLM and SpanMarker. @argilla Video: https://youtu.be/JvLpaYgNd84?feature=shared Notebooks and slides included to try it yourself 🙂
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2024-11-18T13:20:03.000Z
2024-11-18T13:34:10.785Z
[]
/posts/davidberenstein1957/826060405634197
1,845
0
857302200784704
[ { "type": "text", "value": "It's been a while we shipped native quantization support in ", "raw": "It's been a while we shipped native quantization support in ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`diffusers`", "href": null, "resource": null, "url": null, "code": "diffusers", "user": null, "label": null, "lang": null }, { "type": "text", "value": " 🧨", "raw": " 🧨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We currently support ", "raw": "We currently support ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`bistandbytes`", "href": null, "resource": null, "url": null, "code": "bistandbytes", "user": null, "label": null, "lang": null }, { "type": "text", "value": " as the official backend but using others like ", "raw": " as the official backend but using others like ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`torchao`", "href": null, "resource": null, "url": null, "code": "torchao", "user": null, "label": null, "lang": null }, { "type": "text", "value": " is already very simple. ", "raw": " is already very simple. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This post is just a reminder of what's possible:", "raw": "This post is just a reminder of what's possible:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Loading a model with a quantization config", "raw": "1. Loading a model with a quantization config", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Saving a model with quantization config", "raw": "2. Saving a model with quantization config", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. Loading a pre-quantized model", "raw": "3. Loading a pre-quantized model", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4. ", "raw": "4. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`enable_model_cpu_offload()`", "href": null, "resource": null, "url": null, "code": "enable_model_cpu_offload()", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5. Training and loading LoRAs into quantized checkpoints", "raw": "5. Training and loading LoRAs into quantized checkpoints", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Docs:", "raw": "Docs:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/diffusers/main/en/quantization/bitsandbytes", "href": "https://huggingface.co/docs/diffusers/main/en/quantization/bitsandbytes", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
It's been a while we shipped native quantization support in `diffusers` 🧨 We currently support `bistandbytes` as the official backend but using others like `torchao` is already very simple. This post is just a reminder of what's possible: 1. Loading a model with a quantization config 2. Saving a model with quantization config 3. Loading a pre-quantized model 4. `enable_model_cpu_offload()` 5. Training and loading LoRAs into quantized checkpoints Docs: https://huggingface.co/docs/diffusers/main/en/quantization/bitsandbytes
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2024-11-18T10:55:22.000Z
2024-11-18T11:06:39.805Z
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/posts/sayakpaul/857302200784704
2,194
1
110133535234807
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LLaMA Mesh 🔥 Unifying 3D Mesh Generation with Language Models Model: https://huggingface.co/Zhengyi/LLaMA-Mesh Demo: https://huggingface.co/spaces/Zhengyi/LLaMA-Mesh Paper: https://huggingface.co/papers/2411.09595 ✨ Unified 3D generation & text understanding. ✨ 3D meshes as plain text for seamless LLM integration. ✨ High-quality 3D outputs rivaling specialized models.
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2024-11-18T10:13:02.000Z
2024-11-18T10:13:41.841Z
[]
/posts/AdinaY/110133535234807
1,413
0
354288173622044
[ { "type": "text", "value": "A while ago I started experimenting with compiling the Python interpreter to WASM.", "raw": "A while ago I started experimenting with compiling the Python interpreter to WASM.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "To build a secure, fast, and lightweight sandbox for code execution — ideal for running LLM-generated Python code.", "raw": "To build a secure, fast, and lightweight sandbox for code execution — ideal for running LLM-generated Python code.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Send code simply as a POST request", "raw": "- Send code simply as a POST request", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- 1-2ms startup times", "raw": "- 1-2ms startup times", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Hack away:", "raw": "Hack away:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/ErikKaum/runner", "href": "https://github.com/ErikKaum/runner", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
A while ago I started experimenting with compiling the Python interpreter to WASM. To build a secure, fast, and lightweight sandbox for code execution — ideal for running LLM-generated Python code. - Send code simply as a POST request - 1-2ms startup times Hack away: https://github.com/ErikKaum/runner
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2024-11-18T09:32:58.000Z
2024-11-18T09:33:05.453Z
[]
/posts/erikkaum/354288173622044
1,641
0
807842938197371
[ { "type": "text", "value": "NEW MODEL + DATASET! :)", "raw": "NEW MODEL + DATASET! :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out Enigma, our new code-instruct model:", "raw": "Check out Enigma, our new code-instruct model:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- trained on synthetic code-instruct data created with Llama 3.1 405b", "raw": "- trained on synthetic code-instruct data created with Llama 3.1 405b", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- high quality code-instruct within the Llama 3.1 Instruct format", "raw": "- high quality code-instruct within the Llama 3.1 Instruct format", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The model: ", "raw": "The model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma", "href": null, "resource": { "type": "model", "id": "ValiantLabs/Llama3.1-8B-Enigma", "discussionNum": null }, "url": "https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The dataset: ", "raw": "The dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/sequelbox/Tachibana", "href": null, "resource": { "type": "dataset", "id": "sequelbox/Tachibana", "discussionNum": null }, "url": "https://huggingface.co/datasets/sequelbox/Tachibana", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Enjoy! We've got more new datasets and models to follow soon.", "raw": "Enjoy! We've got more new datasets and models to follow soon.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
NEW MODEL + DATASET! :) Check out Enigma, our new code-instruct model: - trained on synthetic code-instruct data created with Llama 3.1 405b - high quality code-instruct within the Llama 3.1 Instruct format The model: https://huggingface.co/ValiantLabs/Llama3.1-8B-Enigma The dataset: https://huggingface.co/datasets/sequelbox/Tachibana Enjoy! We've got more new datasets and models to follow soon.
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[]
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2024-08-11T04:00:04.000Z
2024-08-11T04:00:04.774Z
[]
/posts/sequelbox/807842938197371
853
0
824320656349676
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MISTRAL EAP https://huggingface.co/nroggendorff/mistral-eap
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2024-08-11T02:39:59.000Z
2024-08-14T02:03:58.274Z
[]
/posts/nroggendorff/824320656349676
2,207
0
869444001911097
[ { "type": "text", "value": "Alright Ya'll", "raw": "Alright Ya'll", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I know it's a Saturday, but I decided to release my first Flux Dev Lora. ", "raw": "I know it's a Saturday, but I decided to release my first Flux Dev Lora. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A retrain of my \"Frosting Lane\" model and I am sure the styles will just keep improving. ", "raw": "A retrain of my \"Frosting Lane\" model and I am sure the styles will just keep improving. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have fun! Link Below - Thanks again to ", "raw": "Have fun! Link Below - Thanks again to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@ostris", "href": null, "resource": null, "url": null, "code": null, "user": "ostris", "label": null, "lang": null }, { "type": "text", "value": " for the trainer and Black Forest Labs for the awesome model!", "raw": " for the trainer and Black Forest Labs for the awesome model!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/alvdansen/frosting_lane_flux", "href": null, "resource": { "type": "model", "id": "alvdansen/frosting_lane_flux", "discussionNum": null }, "url": "https://huggingface.co/alvdansen/frosting_lane_flux", "code": null, "user": null, "label": null, "lang": null } ]
Alright Ya'll I know it's a Saturday, but I decided to release my first Flux Dev Lora. A retrain of my "Frosting Lane" model and I am sure the styles will just keep improving. Have fun! Link Below - Thanks again to @ostris for the trainer and Black Forest Labs for the awesome model! https://huggingface.co/alvdansen/frosting_lane_flux
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2024-08-10T23:58:09.000Z
2024-08-10T23:58:09.649Z
[]
/posts/alvdansen/869444001911097
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818558288761504
[ { "type": "text", "value": "Hello Community,", "raw": "Hello Community,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I am seeking guidance on solving a complex problem related to tender categorization. Here's a detailed overview of my requirements and challenges:", "raw": "I am seeking guidance on solving a complex problem related to tender categorization. Here's a detailed overview of my requirements and challenges:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Problem Statement:", "raw": "Problem Statement:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I need to categorize tenders based on their descriptions on a daily basis.", "raw": "I need to categorize tenders based on their descriptions on a daily basis.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I process over 35,000 tenders daily, each potentially belonging to a vast range of categories. Predicting the exact category for each tender is challenging due to the diversity and complexity of categories.", "raw": "I process over 35,000 tenders daily, each potentially belonging to a vast range of categories. Predicting the exact category for each tender is challenging due to the diversity and complexity of categories.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Requirements:", "raw": "Requirements:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model Selection: I require a model that can accurately predict the category of each tender based on its description. I plan to create a dataset from 1 million records in the required format to train the model. Post-training, I also need to optimize the model for performance.", "raw": "Model Selection: I require a model that can accurately predict the category of each tender based on its description. I plan to create a dataset from 1 million records in the required format to train the model. Post-training, I also need to optimize the model for performance.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Training and Optimization: Guidance on the best practices for training and optimizing the model is essential. I am considering Hugging Face’s tools but am unsure about how to effectively use them or any alternatives.", "raw": "Training and Optimization: Guidance on the best practices for training and optimizing the model is essential. I am considering Hugging Face’s tools but am unsure about how to effectively use them or any alternatives.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Local Deployment: If I opt for a local deployment, I need to know what kind of infrastructure or applications are required to run the model on my own servers efficiently.", "raw": "Local Deployment: If I opt for a local deployment, I need to know what kind of infrastructure or applications are required to run the model on my own servers efficiently.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Performance: The model must provide responses quickly, given the high volume of tenders.", "raw": "Performance: The model must provide responses quickly, given the high volume of tenders.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Questions for the Community:", "raw": "Questions for the Community:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model Recommendations:", "raw": "Model Recommendations:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Which model or method would be most suitable for categorizing tenders with diverse categories?", "raw": "Which model or method would be most suitable for categorizing tenders with diverse categories?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What are the best practices for training and optimizing such a model?", "raw": "What are the best practices for training and optimizing such a model?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Infrastructure and Deployment:", "raw": "Infrastructure and Deployment:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What infrastructure or software is needed to deploy a model locally for this purpose?", "raw": "What infrastructure or software is needed to deploy a model locally for this purpose?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How can I ensure the model performs efficiently on my server?", "raw": "How can I ensure the model performs efficiently on my server?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Performance Optimization:", "raw": "Performance Optimization:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What strategies or techniques can I use to achieve quick response times from the model?", "raw": "What strategies or techniques can I use to achieve quick response times from the model?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I appreciate any advice, suggestions, or resources you can provide to help address these challenges.", "raw": "I appreciate any advice, suggestions, or resources you can provide to help address these challenges.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thank you!", "raw": "Thank you!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Best regards,", "raw": "Best regards,", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Sachin Vaishnav", "raw": "Sachin Vaishnav", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hello Community, I am seeking guidance on solving a complex problem related to tender categorization. Here's a detailed overview of my requirements and challenges: Problem Statement: I need to categorize tenders based on their descriptions on a daily basis. I process over 35,000 tenders daily, each potentially belonging to a vast range of categories. Predicting the exact category for each tender is challenging due to the diversity and complexity of categories. Requirements: Model Selection: I require a model that can accurately predict the category of each tender based on its description. I plan to create a dataset from 1 million records in the required format to train the model. Post-training, I also need to optimize the model for performance. Training and Optimization: Guidance on the best practices for training and optimizing the model is essential. I am considering Hugging Face’s tools but am unsure about how to effectively use them or any alternatives. Local Deployment: If I opt for a local deployment, I need to know what kind of infrastructure or applications are required to run the model on my own servers efficiently. Performance: The model must provide responses quickly, given the high volume of tenders. Questions for the Community: Model Recommendations: Which model or method would be most suitable for categorizing tenders with diverse categories? What are the best practices for training and optimizing such a model? Infrastructure and Deployment: What infrastructure or software is needed to deploy a model locally for this purpose? How can I ensure the model performs efficiently on my server? Performance Optimization: What strategies or techniques can I use to achieve quick response times from the model? I appreciate any advice, suggestions, or resources you can provide to help address these challenges. Thank you! Best regards, Sachin Vaishnav
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2024-08-10T15:43:02.000Z
2024-08-10T15:43:02.542Z
[]
/posts/combatsolutions/818558288761504
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[ { "type": "text", "value": "Remember when Claude 3.5 Sonnet by ", "raw": "Remember when Claude 3.5 Sonnet by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@AnthropicAI", "href": null, "resource": null, "url": null, "code": null, "user": "AnthropicAI", "label": null, "lang": null }, { "type": "text", "value": " took the world by storm with Claude Artifacts? 🌍✨", "raw": " took the world by storm with Claude Artifacts? 🌍✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now we have LlamaCoder, an open-source Claude Artifacts app that can generate full React apps and components with Meta-Llama 3.1 405B. 💻 100% free and open source. 🆓", "raw": "Now we have LlamaCoder, an open-source Claude Artifacts app that can generate full React apps and components with Meta-Llama 3.1 405B. 💻 100% free and open source. 🆓", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I like how Llama has now started becoming a placeholder denoting open-source work! 🔓", "raw": "I like how Llama has now started becoming a placeholder denoting open-source work! 🔓", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Originally, Llama was an acronym for Large Language Model Meta AI. 🤖", "raw": "Originally, Llama was an acronym for Large Language Model Meta AI. 🤖", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Nutlope/llamacoder", "href": "https://github.com/Nutlope/llamacoder", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo (by togetherAI): ", "raw": "Demo (by togetherAI): ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://llamacoder.together.ai", "href": "https://llamacoder.together.ai", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Remember when Claude 3.5 Sonnet by @AnthropicAI took the world by storm with Claude Artifacts? 🌍✨ Now we have LlamaCoder, an open-source Claude Artifacts app that can generate full React apps and components with Meta-Llama 3.1 405B. 💻 100% free and open source. 🆓 I like how Llama has now started becoming a placeholder denoting open-source work! 🔓 Originally, Llama was an acronym for Large Language Model Meta AI. 🤖 GitHub: https://github.com/Nutlope/llamacoder Demo (by togetherAI): https://llamacoder.together.ai
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2024-08-10T12:11:41.000Z
2024-08-11T07:40:08.102Z
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/posts/singhsidhukuldeep/646219585332890
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[ { "type": "text", "value": "🚀 Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! 🇹🇷🖼️", "raw": "🚀 Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! 🇹🇷🖼️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, it’s easy to load, fine-tune, and use—no external libraries needed!", "raw": "I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, it’s easy to load, fine-tune, and use—no external libraries needed!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model.", "raw": "Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🎉 Explore it now:", "raw": "🎉 Explore it now:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Model: ", "raw": "- Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ucsahin/TraVisionLM-base", "href": null, "resource": { "type": "model", "id": "ucsahin/TraVisionLM-base", "discussionNum": null }, "url": "https://huggingface.co/ucsahin/TraVisionLM-base", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Demo: ", "raw": "- Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model", "href": "https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Object Detection Finetune: ", "raw": "- Object Detection Finetune: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft", "href": null, "resource": { "type": "model", "id": "ucsahin/TraVisionLM-Object-Detection-ft", "discussionNum": null }, "url": "https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Let’s push Turkish visual language processing forward!", "raw": "Let’s push Turkish visual language processing forward!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "---", "raw": "---", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🚀 TraVisionLM: Türünün İlk Örneği Türkçe Görsel Dil Modelini Sunuyorum! 🇹🇷🖼️", "raw": "🚀 TraVisionLM: Türünün İlk Örneği Türkçe Görsel Dil Modelini Sunuyorum! 🇹🇷🖼️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "TraVisionLM modelini Hugging Face'te yayınladım! 875M parametre ile bu hafif ve verimli model, görüntüye dayalı Türkçe talimatları işlemek için tasarlandı. Transformers kütüphanesiyle tamamen uyumlu, yüklemesi, eğitmesi ve kullanması çok kolay—dış kütüphane gerekmez!", "raw": "TraVisionLM modelini Hugging Face'te yayınladım! 875M parametre ile bu hafif ve verimli model, görüntüye dayalı Türkçe talimatları işlemek için tasarlandı. Transformers kütüphanesiyle tamamen uyumlu, yüklemesi, eğitmesi ve kullanması çok kolay—dış kütüphane gerekmez!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Tek başıma geliştirdiğim TraVisionLM, düşük kaynaklı dillerde araştırmalar için sağlam bir temel sunuyor. Geliştirmeye devam ederken geri bildirimlerinizi bekliyorum.", "raw": "Tek başıma geliştirdiğim TraVisionLM, düşük kaynaklı dillerde araştırmalar için sağlam bir temel sunuyor. Geliştirmeye devam ederken geri bildirimlerinizi bekliyorum.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🎉 Hemen keşfedin:", "raw": "🎉 Hemen keşfedin:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Model: ", "raw": "- Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ucsahin/TraVisionLM-base", "href": null, "resource": { "type": "model", "id": "ucsahin/TraVisionLM-base", "discussionNum": null }, "url": "https://huggingface.co/ucsahin/TraVisionLM-base", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Demo: ", "raw": "- Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model", "href": "https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Obje Tespiti İnce Ayarı: ", "raw": "- Obje Tespiti İnce Ayarı: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft", "href": null, "resource": { "type": "model", "id": "ucsahin/TraVisionLM-Object-Detection-ft", "discussionNum": null }, "url": "https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Türkçe görsel dil işleme sınırlarını birlikte zorlayalım!", "raw": "Türkçe görsel dil işleme sınırlarını birlikte zorlayalım!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
🚀 Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! 🇹🇷🖼️ I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, it’s easy to load, fine-tune, and use—no external libraries needed! Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model. 🎉 Explore it now: - Model: https://huggingface.co/ucsahin/TraVisionLM-base - Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model - Object Detection Finetune: https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft Let’s push Turkish visual language processing forward! --- 🚀 TraVisionLM: Türünün İlk Örneği Türkçe Görsel Dil Modelini Sunuyorum! 🇹🇷🖼️ TraVisionLM modelini Hugging Face'te yayınladım! 875M parametre ile bu hafif ve verimli model, görüntüye dayalı Türkçe talimatları işlemek için tasarlandı. Transformers kütüphanesiyle tamamen uyumlu, yüklemesi, eğitmesi ve kullanması çok kolay—dış kütüphane gerekmez! Tek başıma geliştirdiğim TraVisionLM, düşük kaynaklı dillerde araştırmalar için sağlam bir temel sunuyor. Geliştirmeye devam ederken geri bildirimlerinizi bekliyorum. 🎉 Hemen keşfedin: - Model: https://huggingface.co/ucsahin/TraVisionLM-base - Demo: https://huggingface.co/spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model - Obje Tespiti İnce Ayarı: https://huggingface.co/ucsahin/TraVisionLM-Object-Detection-ft Türkçe görsel dil işleme sınırlarını birlikte zorlayalım!
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2024-08-10T11:45:48.000Z
2024-08-13T02:00:57.022Z
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/posts/ucsahin/355602196650051
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[ { "type": "text", "value": "App is running again after a quick restart.", "raw": "App is running again after a quick restart.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1", "href": null, "resource": { "type": "space", "id": "sourceoftruthdata/sot_autotrain_dreambooth_v1.1", "discussionNum": null }, "url": "https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1", "code": null, "user": null, "label": null, "lang": null } ]
App is running again after a quick restart. https://huggingface.co/spaces/sourceoftruthdata/sot_autotrain_dreambooth_v1.1
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2024-08-09T17:59:30.000Z
2024-08-09T17:59:30.091Z
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/posts/sourceoftruthdata/483352905031375
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[ { "type": "text", "value": "What is the best LLM for RAG systems? 🤔", "raw": "What is the best LLM for RAG systems? 🤔", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In a business setting, it will be the one that gives the best performance at a great price! 💼💰", "raw": "In a business setting, it will be the one that gives the best performance at a great price! 💼💰", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨", "raw": "And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "That's ", "raw": "That's ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Google", "href": null, "resource": null, "url": null, "code": null, "user": "Google", "label": null, "lang": null }, { "type": "text", "value": " Gemini 1.5 Flash! 🚀🌟", "raw": " Gemini 1.5 Flash! 🚀🌟", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖", "raw": "It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "So the base model must be expensive? 💸", "raw": "So the base model must be expensive? 💸", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. 📉💵", "raw": "For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. 📉💵", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "But is it any good? 🤷‍♂️", "raw": "But is it any good? 🤷‍♂️", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. 📊🎯", "raw": "On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. 📊🎯", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖️👌", "raw": "Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖️👌", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Happy tuning... 🎶🔧", "raw": "Happy tuning... 🎶🔧", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Gemini 1.5 Flash: ", "raw": "Gemini 1.5 Flash: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/", "href": "https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " 🔗", "raw": " 🔗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "LLM Hallucination Index: ", "raw": "LLM Hallucination Index: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.rungalileo.io/hallucinationindex", "href": "https://www.rungalileo.io/hallucinationindex", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " 🔗", "raw": " 🔗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
What is the best LLM for RAG systems? 🤔 In a business setting, it will be the one that gives the best performance at a great price! 💼💰 And maybe it should be easy to fine-tune, cheap to fine-tune... FREE to fine-tune? 😲✨ That's @Google Gemini 1.5 Flash! 🚀🌟 It now supports fine-tuning, and the inference cost is the same as the base model! <coughs LORA adopters> 🤭🤖 So the base model must be expensive? 💸 For the base model, the input price is reduced by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens. 📉💵 But is it any good? 🤷‍♂️ On the LLM Hallucination Index, Gemini 1.5 Flash achieved great context adherence scores of 0.94, 1, and 0.92 across short, medium, and long contexts. 📊🎯 Google has finally given a model that is free to tune and offers an excellent balance between performance and cost. ⚖️👌 Happy tuning... 🎶🔧 Gemini 1.5 Flash: https://developers.googleblog.com/en/gemini-15-flash-updates-google-ai-studio-gemini-api/ 🔗 LLM Hallucination Index: https://www.rungalileo.io/hallucinationindex 🔗
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2024-08-09T11:23:57.000Z
2024-08-11T10:39:43.580Z
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/posts/singhsidhukuldeep/668434057161902
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[ { "type": "text", "value": "I've built a space for creating prompts for FLUX", "raw": "I've built a space for creating prompts for FLUX", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator", "href": null, "resource": { "type": "space", "id": "gokaygokay/FLUX-Prompt-Generator", "discussionNum": null }, "url": "https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts. ", "raw": "You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And you can combine all of them with custom prompts using llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo).", "raw": "And you can combine all of them with custom prompts using llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun!", "raw": "The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And i've created some other spaces for using FLUX models with captioners and enhancers.", "raw": "And i've created some other spaces for using FLUX models with captioners and enhancers.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- ", "raw": "- ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/gokaygokay/FLUX.1-dev-with-Captioner", "href": null, "resource": { "type": "space", "id": "gokaygokay/FLUX.1-dev-with-Captioner", "discussionNum": null }, "url": "https://huggingface.co/spaces/gokaygokay/FLUX.1-dev-with-Captioner", "code": null, "user": null, "label": null, "lang": null } ]
I've built a space for creating prompts for FLUX https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator You can create long prompts from images or simple words. Enhance your short prompts with prompt enhancer. You can configure various settings such as artform, photo type, character details, scene details, style, and artist to create tailored prompts. And you can combine all of them with custom prompts using llms (Mixtral, Mistral, Llama 3, and Mistral-Nemo). The UI is a bit complex, but it includes almost everything you need. Choosing random option is the most fun! And i've created some other spaces for using FLUX models with captioners and enhancers. - https://huggingface.co/spaces/gokaygokay/FLUX.1-dev-with-Captioner
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2024-08-08T22:54:04.000Z
2024-09-01T00:51:18.623Z
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/posts/gokaygokay/498726217882541
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[ { "type": "text", "value": "New smol-vision tutorial dropped: QLoRA fine-tuning IDEFICS3-Llama 8B on VQAv2 🐶", "raw": "New smol-vision tutorial dropped: QLoRA fine-tuning IDEFICS3-Llama 8B on VQAv2 🐶", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook 📖", "raw": "Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook 📖", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Fine-tuning notebook: ", "raw": "Fine-tuning notebook: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb", "href": "https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Resulting model: ", "raw": "Resulting model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/merve/idefics3llama-vqav2", "href": null, "resource": { "type": "model", "id": "merve/idefics3llama-vqav2", "discussionNum": null }, "url": "https://huggingface.co/merve/idefics3llama-vqav2", "code": null, "user": null, "label": null, "lang": null } ]
New smol-vision tutorial dropped: QLoRA fine-tuning IDEFICS3-Llama 8B on VQAv2 🐶 Learn how to efficiently fine-tune the latest IDEFICS3-Llama on visual question answering in this notebook 📖 Fine-tuning notebook: https://github.com/merveenoyan/smol-vision/blob/main/Idefics_FT.ipynb Resulting model: https://huggingface.co/merve/idefics3llama-vqav2
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2024-08-08T18:02:07.000Z
2024-08-12T14:06:33.147Z
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/posts/merve/349537950589417
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[ { "type": "text", "value": "✨The STABLE IMAGINE !!✨", "raw": "✨The STABLE IMAGINE !!✨", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🍺Space: ", "raw": "🍺Space: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE", "href": "https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "↗️The specific LoRA in the space that requires appropriate trigger words brings good results.", "raw": "↗️The specific LoRA in the space that requires appropriate trigger words brings good results.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📒 Articles: ", "raw": "📒 Articles: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/prithivMLmods/lora-adp-01", "href": "https://huggingface.co/blog/prithivMLmods/lora-adp-01", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "**Description and Utility Functions **", "raw": "**Description and Utility Functions **", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "✅ Most likely image generation ", "raw": "✅ Most likely image generation ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "☑️ Most accurate trigger words expected ", "raw": "☑️ Most accurate trigger words expected ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "✅ Each designed to capture different artistic elements ", "raw": "✅ Each designed to capture different artistic elements ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "☑️ Specialized styles and characteristics ", "raw": "☑️ Specialized styles and characteristics ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "✅ Flexible to design what is needed (keyword-centric) ", "raw": "✅ Flexible to design what is needed (keyword-centric) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "☑️ Increasing productivity", "raw": "☑️ Increasing productivity", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🫙Repository: ", "raw": "🫙Repository: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/prithivsakthiur/gen-vision", "href": "https://github.com/prithivsakthiur/gen-vision", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📔Colab Link: ", "raw": "📔Colab Link: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1axA0pU--32t4a8AHiVlt6zyl8gRfiXKs", "href": "https://colab.research.google.com/drive/1axA0pU--32t4a8AHiVlt6zyl8gRfiXKs", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "*️⃣Notebook: ", "raw": "*️⃣Notebook: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE/blob/main/Gen_Vision.ipynb", "href": "https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE/blob/main/Gen_Vision.ipynb", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n lora_options = {\n \"Realism (face/character)\": (\"prithivMLmods/Canopus-Realism-LoRA\", \"Canopus-Realism-LoRA.safetensors\", \"rlms\"),\n \"Pixar (art/toons)\": (\"prithivMLmods/Canopus-Pixar-Art\", \"Canopus-Pixar-Art.safetensors\", \"pixar\"),\n \"Photoshoot (camera/film)\": (\"prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA\", \"Canopus-Photo-Shoot-Mini-LoRA.safetensors\", \"photo\"),\n \"Clothing (hoodies/pant/shirts)\": (\"prithivMLmods/Canopus-Clothing-Adp-LoRA\", \"Canopus-Dress-Clothing-LoRA.safetensors\", \"clth\"),\n }\n .\n .\n .\nfor model_name, weight_name, adapter_name in lora_options.values():\n pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name)\npipe.to(\"cuda\")\n\n```", "href": null, "resource": null, "url": null, "code": " lora_options = {\n \"Realism (face/character)\": (\"prithivMLmods/Canopus-Realism-LoRA\", \"Canopus-Realism-LoRA.safetensors\", \"rlms\"),\n \"Pixar (art/toons)\": (\"prithivMLmods/Canopus-Pixar-Art\", \"Canopus-Pixar-Art.safetensors\", \"pixar\"),\n \"Photoshoot (camera/film)\": (\"prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA\", \"Canopus-Photo-Shoot-Mini-LoRA.safetensors\", \"photo\"),\n \"Clothing (hoodies/pant/shirts)\": (\"prithivMLmods/Canopus-Clothing-Adp-LoRA\", \"Canopus-Dress-Clothing-LoRA.safetensors\", \"clth\"),\n }\n .\n .\n .\nfor model_name, weight_name, adapter_name in lora_options.values():\n pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name)\npipe.to(\"cuda\")", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
✨The STABLE IMAGINE !!✨ 🍺Space: https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE ↗️The specific LoRA in the space that requires appropriate trigger words brings good results. 📒 Articles: https://huggingface.co/blog/prithivMLmods/lora-adp-01 **Description and Utility Functions ** ✅ Most likely image generation ☑️ Most accurate trigger words expected ✅ Each designed to capture different artistic elements ☑️ Specialized styles and characteristics ✅ Flexible to design what is needed (keyword-centric) ☑️ Increasing productivity 🫙Repository: https://github.com/prithivsakthiur/gen-vision 📔Colab Link: https://colab.research.google.com/drive/1axA0pU--32t4a8AHiVlt6zyl8gRfiXKs *️⃣Notebook: https://huggingface.co/spaces/prithivMLmods/STABLE-IMAGINE/blob/main/Gen_Vision.ipynb ``` lora_options = { "Realism (face/character)": ("prithivMLmods/Canopus-Realism-LoRA", "Canopus-Realism-LoRA.safetensors", "rlms"), "Pixar (art/toons)": ("prithivMLmods/Canopus-Pixar-Art", "Canopus-Pixar-Art.safetensors", "pixar"), "Photoshoot (camera/film)": ("prithivMLmods/Canopus-Photo-Shoot-Mini-LoRA", "Canopus-Photo-Shoot-Mini-LoRA.safetensors", "photo"), "Clothing (hoodies/pant/shirts)": ("prithivMLmods/Canopus-Clothing-Adp-LoRA", "Canopus-Dress-Clothing-LoRA.safetensors", "clth"), } . . . for model_name, weight_name, adapter_name in lora_options.values(): pipe.load_lora_weights(model_name, weight_name=weight_name, adapter_name=adapter_name) pipe.to("cuda") ```
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2024-08-08T17:32:09.000Z
2024-08-13T00:40:32.023Z
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/posts/prithivMLmods/346585831040839
2,129
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"raw": "1-Click to install with instructions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All tested and verified", "raw": "All tested and verified", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Windows tutorial : ", "raw": "Windows tutorial : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/FPtpNrmuwXk", "href": "https://youtu.be/FPtpNrmuwXk", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Cloud (RunPod, Massed Compute & free Kaggle account) tutorial : ", "raw": "Cloud (RunPod, Massed Compute & free Kaggle account) tutorial : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/wG7oPp01COg", "href": "https://youtu.be/wG7oPp01COg", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Making XPose / UniPose / ops library compiling working was a challenge on Massed Compute and Kaggle. ", "raw": "Making XPose / UniPose / ops library compiling working was a challenge on Massed Compute and Kaggle. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Live Portrait Updated to V5 Animals Live animation added All of the main repo changes and improvements added to our modified and improve app Link : https://patreon.com/posts/107609670 Works perfect on Massed Compute, RunPod, free Kaggle account and Windows 1-Click to install with instructions All tested and verified Windows tutorial : https://youtu.be/FPtpNrmuwXk Cloud (RunPod, Massed Compute & free Kaggle account) tutorial : https://youtu.be/wG7oPp01COg Making XPose / UniPose / ops library compiling working was a challenge on Massed Compute and Kaggle.
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2024-08-08T12:15:06.000Z
2024-08-08T12:15:06.468Z
[]
/posts/MonsterMMORPG/602569236896247
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[ { "type": "text", "value": "PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.", "raw": "PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. ", "raw": "The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance - this helps reduce compute and inference cost.", "raw": "It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance - this helps reduce compute and inference cost.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Code: ", "raw": "Code: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/Jaykef/ai-algorithms", "href": "https://github.com/Jaykef/ai-algorithms", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/pdf/2301.13142", "href": "https://arxiv.org/pdf/2301.13142", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper. The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance - this helps reduce compute and inference cost. Code: https://github.com/Jaykef/ai-algorithms Paper: https://arxiv.org/pdf/2301.13142
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2024-08-08T12:11:15.000Z
2024-08-10T08:38:22.978Z
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/posts/Jaward/678534702748580
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150307217117480
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🔥 New state of the art model for background removal is out 🤗 You can try the model at https://huggingface.co/ZhengPeng7/BiRefNet 📈 model shows impressive results outperforming https://huggingface.co/briaai/RMBG-1.4 🚀 you can try out the model in: https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo 📃paper: https://huggingface.co/papers/2401.03407
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2024-08-08T10:54:38.000Z
2024-08-08T13:13:13.836Z
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/posts/not-lain/150307217117480
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https://huggingface.co/JoseRFJunior/TransNAR https://github.com/JoseRFJuniorLLMs/TransNAR https://arxiv.org/html/2406.09308v1 TransNAR hybrid architecture. Similar to Alayrac et al, we interleave existing Transformer layers with gated cross-attention layers which enable information to flow from the NAR to the Transformer. We generate queries from tokens while we obtain keys and values from nodes and edges of the graph. The node and edge embeddings are obtained by running the NAR on the graph version of the reasoning task to be solved. When experimenting with pre-trained Transformers, we initially close the cross-attention gate, in order to fully preserve the language model’s internal knowledge at the beginning of training.
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2024-08-08T10:18:24.000Z
2024-08-08T10:19:30.295Z
[]
/posts/JoseRFJunior/384554398233072
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[ { "type": "text", "value": "Remember when ", "raw": "Remember when ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@mistralAI", "href": null, "resource": null, "url": null, "code": null, "user": "mistralAI", "label": null, "lang": null }, { "type": "text", "value": " said large enough and casually dropped Mistral-Large-Instruct-2407? 🤯🚀", "raw": " said large enough and casually dropped Mistral-Large-Instruct-2407? 🤯🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's now on ", "raw": "It's now on ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "http://lmsys.org", "href": "http://lmsys.org", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "! 🌐 It works amazing for instruction following, hard prompts, coding, and longer queries with only 123 billion parameters. 💡💻", "raw": "! 🌐 It works amazing for instruction following, hard prompts, coding, and longer queries with only 123 billion parameters. 💡💻", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It outperforms GPT4-Turbo and Claude 3 Opus on Coding, Hard Prompts, Math, and Longer Query categories. 📈🔢", "raw": "It outperforms GPT4-Turbo and Claude 3 Opus on Coding, Hard Prompts, Math, and Longer Query categories. 📈🔢", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It also outperforms Llama 3.1 405B on Instruction Following while being 3x smaller. 🐎🔍", "raw": "It also outperforms Llama 3.1 405B on Instruction Following while being 3x smaller. 🐎🔍", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It also does exceedingly well on the Ai2 ZebraLogic logistic reasoning benchmark despite being much smaller than the other models. 🦓🤔", "raw": "It also does exceedingly well on the Ai2 ZebraLogic logistic reasoning benchmark despite being much smaller than the other models. 🦓🤔", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Mistral is not here to take part but to take over! 🏆🌟", "raw": "Mistral is not here to take part but to take over! 🏆🌟", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mistral.ai/news/mistral-large-2407/", "href": "https://mistral.ai/news/mistral-large-2407/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Remember when @mistralAI said large enough and casually dropped Mistral-Large-Instruct-2407? 🤯🚀 It's now on http://lmsys.org! 🌐 It works amazing for instruction following, hard prompts, coding, and longer queries with only 123 billion parameters. 💡💻 It outperforms GPT4-Turbo and Claude 3 Opus on Coding, Hard Prompts, Math, and Longer Query categories. 📈🔢 It also outperforms Llama 3.1 405B on Instruction Following while being 3x smaller. 🐎🔍 It also does exceedingly well on the Ai2 ZebraLogic logistic reasoning benchmark despite being much smaller than the other models. 🦓🤔 Mistral is not here to take part but to take over! 🏆🌟 Model: https://mistral.ai/news/mistral-large-2407/
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2024-08-08T09:13:56.000Z
2024-08-08T09:13:56.197Z
[]
/posts/singhsidhukuldeep/237798706580957
648
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770990891104334
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hello everyone, today I have been working on a project https://huggingface.co/spaces/Blane187/rvc-demo, a demo of rvc using pip, this project is still a demo though (I don't have a beta tester lol)
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2024-08-08T00:36:03.000Z
2024-08-12T06:51:38.252Z
[]
/posts/Blane187/770990891104334
1,354
1
588623583782274
[ { "type": "text", "value": "⚡ My PhD thesis, “Scalable Nested Optimization for Deep Learning,” is now on arXiv! ⚡", "raw": "⚡ My PhD thesis, “Scalable Nested Optimization for Deep Learning,” is now on arXiv! ⚡", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "tl;dr: We develop various optimization tools with highlights, including:", "raw": "tl;dr: We develop various optimization tools with highlights, including:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "· Making the momentum coefficient complex for adversarial games like GANs.", "raw": "· Making the momentum coefficient complex for adversarial games like GANs.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "· Optimizing millions of hyperparameters using implicit differentiation.", "raw": "· Optimizing millions of hyperparameters using implicit differentiation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "· Tuning hyperparameters using hypernetworks.", "raw": "· Tuning hyperparameters using hypernetworks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "· Differentiably finding bifurcations in optimization for diverse solutions.", "raw": "· Differentiably finding bifurcations in optimization for diverse solutions.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2407.01526", "href": "https://arxiv.org/abs/2407.01526", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
⚡ My PhD thesis, “Scalable Nested Optimization for Deep Learning,” is now on arXiv! ⚡ tl;dr: We develop various optimization tools with highlights, including: · Making the momentum coefficient complex for adversarial games like GANs. · Optimizing millions of hyperparameters using implicit differentiation. · Tuning hyperparameters using hypernetworks. · Differentiably finding bifurcations in optimization for diverse solutions. https://arxiv.org/abs/2407.01526
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2024-08-07T21:26:07.000Z
2024-08-09T08:04:08.535Z
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/posts/lorraine2/588623583782274
2,694
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116037709746691
[ { "type": "text", "value": "Is your summer reading list still empty? Curious if an LLM can generate a book blurb you'd enjoy and help build a KTO preference dataset at the same time? ", "raw": "Is your summer reading list still empty? Curious if an LLM can generate a book blurb you'd enjoy and help build a KTO preference dataset at the same time? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A demo using Hugging Face Spaces and Gradio to collect LLM output preferences: ", "raw": "A demo using Hugging Face Spaces and Gradio to collect LLM output preferences: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/davanstrien/would-you-read-it", "href": null, "resource": { "type": "space", "id": "davanstrien/would-you-read-it", "discussionNum": null }, "url": "https://huggingface.co/spaces/davanstrien/would-you-read-it", "code": null, "user": null, "label": null, "lang": null } ]
Is your summer reading list still empty? Curious if an LLM can generate a book blurb you'd enjoy and help build a KTO preference dataset at the same time? A demo using Hugging Face Spaces and Gradio to collect LLM output preferences: https://huggingface.co/spaces/davanstrien/would-you-read-it
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2024-08-07T14:37:24.000Z
2024-08-08T23:35:32.374Z
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/posts/davanstrien/116037709746691
3,150
1
939202255393950
[ { "type": "text", "value": "Just released: Shining Valiant 2 for Llama 3.1 8b!", "raw": "Just released: Shining Valiant 2 for Llama 3.1 8b!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- the first SV at 8b size, using the best 8b model", "raw": "- the first SV at 8b size, using the best 8b model", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- newest version of the SV dataset improves specialist knowledge and response consistency", "raw": "- newest version of the SV dataset improves specialist knowledge and response consistency", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3.1 70b will be coming but our next releases will focus on expanding the Build Tools lineup. Get ready for some open-source synthetic datasets made with 3.1 405, coming VERY soon :)", "raw": "3.1 70b will be coming but our next releases will focus on expanding the Build Tools lineup. Get ready for some open-source synthetic datasets made with 3.1 405, coming VERY soon :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "get SV2: ", "raw": "get SV2: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2", "href": null, "resource": { "type": "model", "id": "ValiantLabs/Llama3.1-8B-ShiningValiant2", "discussionNum": null }, "url": "https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Just released: Shining Valiant 2 for Llama 3.1 8b! - the first SV at 8b size, using the best 8b model - newest version of the SV dataset improves specialist knowledge and response consistency 3.1 70b will be coming but our next releases will focus on expanding the Build Tools lineup. Get ready for some open-source synthetic datasets made with 3.1 405, coming VERY soon :) get SV2: https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2
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2024-08-07T13:31:09.000Z
2024-08-09T00:19:40.546Z
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/posts/sequelbox/939202255393950
1,693
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428754145152300
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@warmshao Hello. do you do any paid consulting?
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2024-08-07T12:52:13.000Z
2024-08-07T12:57:14.876Z
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/posts/gadget01/428754145152300
589
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distilabel 1.3.0 is out! This release contains many core improvements and new tasks that help us building https://huggingface.co/datasets/argilla/magpie-ultra-v0.1! Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features! Check the new release in GitHub: https://github.com/argilla-io/distilabel
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2024-08-07T10:12:33.000Z
2024-08-07T10:12:33.695Z
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/posts/gabrielmbmb/323184025352600
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[ { "type": "text", "value": " 🚀 We are thrilled to introduce TextImage Data Augmentation, developed in collaboration with Albumentations AI! ✨ This multimodal technique modifies document images and text simultaneously, enhancing Vision Language Models (VLMs) for high-text datasets.", "raw": " 🚀 We are thrilled to introduce TextImage Data Augmentation, developed in collaboration with Albumentations AI! ✨ This multimodal technique modifies document images and text simultaneously, enhancing Vision Language Models (VLMs) for high-text datasets.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "👩‍💻 Learn how this innovative approach can improve your document AI projects by checking out our full blog post here: ", "raw": "👩‍💻 Learn how this innovative approach can improve your document AI projects by checking out our full blog post here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/doc_aug_hf_alb", "href": "https://huggingface.co/blog/doc_aug_hf_alb", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
🚀 We are thrilled to introduce TextImage Data Augmentation, developed in collaboration with Albumentations AI! ✨ This multimodal technique modifies document images and text simultaneously, enhancing Vision Language Models (VLMs) for high-text datasets. 👩‍💻 Learn how this innovative approach can improve your document AI projects by checking out our full blog post here: https://huggingface.co/blog/doc_aug_hf_alb
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2024-08-07T06:18:54.000Z
2024-08-08T04:29:01.150Z
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/posts/danaaubakirova/387162624073548
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[ { "type": "text", "value": "🗓️ Remember when last April, ", "raw": "🗓️ Remember when last April, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Meta", "href": null, "resource": null, "url": null, "code": null, "user": "Meta", "label": null, "lang": null }, { "type": "text", "value": " released Segment Anything Model (SAM) paper and it was too good to be true. 🤯", "raw": " released Segment Anything Model (SAM) paper and it was too good to be true. 🤯", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! 🚀", "raw": "They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! 🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. 🖼️📹", "raw": "SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. 🖼️📹", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. 🎭", "raw": "SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. 🎭", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! 🧠", "raw": "The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! 🧠", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "They have also released the dataset, SA-V ", "raw": "They have also released the dataset, SA-V ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! 📊", "raw": "This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! 📊", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📄 Paper: ", "raw": "📄 Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/", "href": "https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📝 Blog: ", "raw": "📝 Blog: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://ai.meta.com/sam2/", "href": "https://ai.meta.com/sam2/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🔗 Demo: ", "raw": "🔗 Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://sam2.metademolab.com/demo", "href": "https://sam2.metademolab.com/demo", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "💾 Model Weights: ", "raw": "💾 Model Weights: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh", "href": "https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📁 Dataset: ", "raw": "📁 Dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://ai.meta.com/datasets/segment-anything-video-downloads/", "href": "https://ai.meta.com/datasets/segment-anything-video-downloads/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
🗓️ Remember when last April, @Meta released Segment Anything Model (SAM) paper and it was too good to be true. 🤯 They have now released Segment Anything Model 2 (SAM 2) and it's mind-blowingly great! 🚀 SAM 2 is the first unified model for segmenting objects across images and videos. You can use a click, box, or mask as the input to select an object on any image or frame of video. 🖼️📹 SAM consists of an image encoder to encode images, a prompt encoder to encode prompts, then outputs of these two are given to a mask decoder to generate masks. 🎭 The biggest jump of SAM2 from SAM is using memory to have consistent masking across frames! They call it masklet prediction! 🧠 They have also released the dataset, SA-V This dataset is truly huge, with 190.9K manual annotations and 451.7K automatic! 📊 📄 Paper: https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/ 📝 Blog: https://ai.meta.com/sam2/ 🔗 Demo: https://sam2.metademolab.com/demo 💾 Model Weights: https://github.com/facebookresearch/segment-anything-2/blob/main/checkpoints/download_ckpts.sh 📁 Dataset: https://ai.meta.com/datasets/segment-anything-video-downloads/
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2024-08-06T22:15:44.000Z
2024-08-08T01:00:17.544Z
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You can now embed your heatmap anywhere with a simple change :) Currently just supports model creation! You can duplicate the space and create your own here: https://huggingface.co/spaces/cfahlgren1/my-heatmap
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2024-08-06T20:22:43.000Z
2024-08-06T21:36:01.520Z
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Idefics3-Llama is out! 💥💥 Model: https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3 Demo: https://huggingface.co/spaces/HuggingFaceM4/idefics3 It's a multimodal model based on Llama 3.1 that accepts an arbitrary number of interleaved images with text with a huge context window (10k tokens!) ✨ Supported by Hugging Face transformers 🤗
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2024-08-06T19:08:00.000Z
2024-08-06T22:26:56.332Z
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[ { "type": "text", "value": "📫 AI in the news today: Struggling AI startups, Figure 0 robot, chipmakers", "raw": "📫 AI in the news today: Struggling AI startups, Figure 0 robot, chipmakers", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- OpenAI Co-Founders Schulman and Brockman Step Back", "raw": "- OpenAI Co-Founders Schulman and Brockman Step Back", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://finance.yahoo.com/news/openai-co-founders-schulman-brockman-010542796.html", "href": "https://finance.yahoo.com/news/openai-co-founders-schulman-brockman-010542796.html", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Struggling AI Startups Look for a Bailout from Big Tech", "raw": "- Struggling AI Startups Look for a Bailout from Big Tech", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "\"More exits—either pseudo-acquisitions or real ones—are coming, investors say, as a bubble built by the excitement around generative AI is showing signs of peaking.\"", "raw": "\"More exits—either pseudo-acquisitions or real ones—are coming, investors say, as a bubble built by the excitement around generative AI is showing signs of peaking.\"", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.wsj.com/tech/ai/struggling-ai-startups-look-for-a-bailout-from-big-tech-3e635927?mod=rss_Technology", "href": "https://www.wsj.com/tech/ai/struggling-ai-startups-look-for-a-bailout-from-big-tech-3e635927?mod=rss_Technology", "resource": null, "url": null, "code": 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"https://www.theinformation.com/articles/did-google-just-pay-2-5-billion-to-hire-characters-ceo", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Figure’s new humanoid robot leverages OpenAI for natural speech conversations", "raw": "- Figure’s new humanoid robot leverages OpenAI for natural speech conversations", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Figure has unveiled its latest humanoid robot, the Figure 02.", "raw": "Figure has unveiled its latest humanoid robot, the Figure 02.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The most notable addition this time out arrives by way a longstanding partnership with OpenAI, which helped Figure raise a $675 million Series B back in February, valuing the South Bay firm at $2.6 billion.", "raw": "The most notable addition this time out arrives by way a longstanding partnership with OpenAI, which helped Figure raise a $675 million Series B back in February, valuing the South Bay firm at $2.6 billion.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://techcrunch.com/2024/08/06/figures-new-humanoid-robot-leverages-openai-for-natural-speech-conversations/", "href": "https://techcrunch.com/2024/08/06/figures-new-humanoid-robot-leverages-openai-for-natural-speech-conversations/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- World’s Five Leading Chipmakers Have Now Promised U.S. Investment", "raw": "- World’s Five Leading Chipmakers Have Now Promised U.S. Investment", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The Biden administration award up to $450 million in grants to a South Korean chipmaker, SK Hynix, to help build its new chip facility in Indiana", "raw": "The Biden administration award up to $450 million in grants to a South Korean chipmaker, SK Hynix, to help build its new chip facility in Indiana", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The US now has commitments from all five of the world’s leading-edge semiconductor manufacturers to construct chip plants in theUS with financial assistance from the administration", "raw": "The US now has commitments from all five of the world’s leading-edge semiconductor manufacturers to construct chip plants in theUS with financial assistance from the administration", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.nytimes.com/2024/08/06/business/economy/chipmakers-promise-investment.html", "href": "https://www.nytimes.com/2024/08/06/business/economy/chipmakers-promise-investment.html", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
📫 AI in the news today: Struggling AI startups, Figure 0 robot, chipmakers - OpenAI Co-Founders Schulman and Brockman Step Back https://finance.yahoo.com/news/openai-co-founders-schulman-brockman-010542796.html - Struggling AI Startups Look for a Bailout from Big Tech "More exits—either pseudo-acquisitions or real ones—are coming, investors say, as a bubble built by the excitement around generative AI is showing signs of peaking." https://www.wsj.com/tech/ai/struggling-ai-startups-look-for-a-bailout-from-big-tech-3e635927?mod=rss_Technology - Did Google Just Pay $2.5 Billion to Hire Character's CEO? https://www.theinformation.com/articles/did-google-just-pay-2-5-billion-to-hire-characters-ceo - Figure’s new humanoid robot leverages OpenAI for natural speech conversations Figure has unveiled its latest humanoid robot, the Figure 02. The most notable addition this time out arrives by way a longstanding partnership with OpenAI, which helped Figure raise a $675 million Series B back in February, valuing the South Bay firm at $2.6 billion. https://techcrunch.com/2024/08/06/figures-new-humanoid-robot-leverages-openai-for-natural-speech-conversations/ - World’s Five Leading Chipmakers Have Now Promised U.S. Investment The Biden administration award up to $450 million in grants to a South Korean chipmaker, SK Hynix, to help build its new chip facility in Indiana The US now has commitments from all five of the world’s leading-edge semiconductor manufacturers to construct chip plants in theUS with financial assistance from the administration https://www.nytimes.com/2024/08/06/business/economy/chipmakers-promise-investment.html
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2024-08-06T14:37:20.000Z
2024-08-06T14:37:20.543Z
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/posts/fdaudens/945200563183685
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[ { "type": "text", "value": "🚀 Introducing The Open Language Models List", "raw": "🚀 Introducing The Open Language Models List", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is a work-in-progress list of open language models with permissive licenses such as MIT, Apache 2.0, or other similar licenses. ", "raw": "This is a work-in-progress list of open language models with permissive licenses such as MIT, Apache 2.0, or other similar licenses. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The list is not limited to only autoregressive models or even only transformers models, and it includes many SSMs, and SSM-Transformers hybrids.", "raw": "The list is not limited to only autoregressive models or even only transformers models, and it includes many SSMs, and SSM-Transformers hybrids.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🤗 Contributions, corrections, and feedback are very welcome!", "raw": "🤗 Contributions, corrections, and feedback are very welcome!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The Open Language Models List: ", "raw": "The Open Language Models List: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/mmhamdy/open-language-models", "href": "https://github.com/mmhamdy/open-language-models", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
🚀 Introducing The Open Language Models List This is a work-in-progress list of open language models with permissive licenses such as MIT, Apache 2.0, or other similar licenses. The list is not limited to only autoregressive models or even only transformers models, and it includes many SSMs, and SSM-Transformers hybrids. 🤗 Contributions, corrections, and feedback are very welcome! The Open Language Models List: https://github.com/mmhamdy/open-language-models
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2024-08-05T23:35:40.000Z
2024-08-06T10:03:14.103Z
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[ { "type": "text", "value": "Who wants to take a stab at explaining this one? SPOILER ALERT: You CANNOT transfer an image Jailbreak from one model to another. Why in the world you cannot do this when you can transfer learn literally everything else? You tell me, experts.", "raw": "Who wants to take a stab at explaining this one? SPOILER ALERT: You CANNOT transfer an image Jailbreak from one model to another. Why in the world you cannot do this when you can transfer learn literally everything else? You tell me, experts.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2407.15211", "href": "https://arxiv.org/abs/2407.15211", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Who wants to take a stab at explaining this one? SPOILER ALERT: You CANNOT transfer an image Jailbreak from one model to another. Why in the world you cannot do this when you can transfer learn literally everything else? You tell me, experts. https://arxiv.org/abs/2407.15211
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2024-08-05T20:52:25.000Z
2024-08-12T17:18:50.933Z
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/posts/TuringsSolutions/172266190682365
1,032
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387979270629421
[ { "type": "text", "value": "Looks like ", "raw": "Looks like ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Google", "href": null, "resource": null, "url": null, "code": null, "user": "Google", "label": null, "lang": null }, { "type": "text", "value": " is still not satisfied with Gemini 1.5 Pro! 😲", "raw": " is still not satisfied with Gemini 1.5 Pro! 😲", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Good folks at ", "raw": "Good folks at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@GoogleDeepMind", "href": null, "resource": null, "url": null, "code": null, "user": "GoogleDeepMind", "label": null, "lang": null }, { "type": "text", "value": " quietly updated the already good Gemini 1.5 Pro to Gemini-1.5-Pro-Experiment-0801 🚀", "raw": " quietly updated the already good Gemini 1.5 Pro to Gemini-1.5-Pro-Experiment-0801 🚀", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Unremarkable naming aside, the model itself outperforms GPT-4o, Claude-3.5, and LLama 3.1 on LMSYS and the Vision Leaderboard. 🌟", "raw": "Unremarkable naming aside, the model itself outperforms GPT-4o, Claude-3.5, and LLama 3.1 on LMSYS and the Vision Leaderboard. 🌟", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Gemini-1.5-Pro-Experiment-0801 is great at almost everything, multi-lingual tasks, Maths, understanding, and coding. 🌐📚💻", "raw": "Gemini-1.5-Pro-Experiment-0801 is great at almost everything, multi-lingual tasks, Maths, understanding, and coding. 🌐📚💻", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Although in my testing, I felt Claude-3.5 was slightly better at coding! 👨‍💻🤔", "raw": "Although in my testing, I felt Claude-3.5 was slightly better at coding! 👨‍💻🤔", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Also, still cannot find an LLM that can solve the \"Strawberry prompt\"! 🍓❓", "raw": "Also, still cannot find an LLM that can solve the \"Strawberry prompt\"! 🍓❓", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "\"\"\"", "raw": "\"\"\"", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How many R's are there in Strawberry? ", "raw": "How many R's are there in Strawberry? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Also, write Strawberry with all r's in brackets ", "raw": "Also, write Strawberry with all r's in brackets ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "\"\"\"", "raw": "\"\"\"", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Try here: ", "raw": "Try here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://aistudio.google.com/app/prompts/new_chat", "href": "https://aistudio.google.com/app/prompts/new_chat", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Looks like @Google is still not satisfied with Gemini 1.5 Pro! 😲 Good folks at @GoogleDeepMind quietly updated the already good Gemini 1.5 Pro to Gemini-1.5-Pro-Experiment-0801 🚀 Unremarkable naming aside, the model itself outperforms GPT-4o, Claude-3.5, and LLama 3.1 on LMSYS and the Vision Leaderboard. 🌟 Gemini-1.5-Pro-Experiment-0801 is great at almost everything, multi-lingual tasks, Maths, understanding, and coding. 🌐📚💻 Although in my testing, I felt Claude-3.5 was slightly better at coding! 👨‍💻🤔 Also, still cannot find an LLM that can solve the "Strawberry prompt"! 🍓❓ """ How many R's are there in Strawberry? Also, write Strawberry with all r's in brackets """ Try here: https://aistudio.google.com/app/prompts/new_chat
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2024-08-05T20:34:39.000Z
2024-08-06T04:18:00.176Z
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/posts/singhsidhukuldeep/387979270629421
753
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492540057768389
[ { "type": "text", "value": "We are offering a free trial of the Tumeryk Gen AI Security Studio with 2 million tokens for the hugging face community. You can run a vulnerability scan of the LLM and get a security score to fix the vulnerabilities via the built in policy editing tools for configuring the necessary guardrail policies. ", "raw": "We are offering a free trial of the Tumeryk Gen AI Security Studio with 2 million tokens for the hugging face community. You can run a vulnerability scan of the LLM and get a security score to fix the vulnerabilities via the built in policy editing tools for configuring the necessary guardrail policies. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Protect against jailbreaks, moderate content and manage hallucinations.", "raw": "Protect against jailbreaks, moderate content and manage hallucinations.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.tumeryk.com/sign-up", "href": "https://www.tumeryk.com/sign-up", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " to get an account or you can get an account from the Amazon Marketplace ", "raw": " to get an account or you can get an account from the Amazon Marketplace ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://aws.amazon.com/marketplace/pp/prodview-c6ywcyjefl3hu?sr=0-2&ref_=beagle&applicationId=AWSMPContessa", "href": "https://aws.amazon.com/marketplace/pp/prodview-c6ywcyjefl3hu?sr=0-2&ref_=beagle&applicationId=AWSMPContessa", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
We are offering a free trial of the Tumeryk Gen AI Security Studio with 2 million tokens for the hugging face community. You can run a vulnerability scan of the LLM and get a security score to fix the vulnerabilities via the built in policy editing tools for configuring the necessary guardrail policies. Protect against jailbreaks, moderate content and manage hallucinations. https://www.tumeryk.com/sign-up to get an account or you can get an account from the Amazon Marketplace https://aws.amazon.com/marketplace/pp/prodview-c6ywcyjefl3hu?sr=0-2&ref_=beagle&applicationId=AWSMPContessa
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2024-08-05T19:51:28.000Z
2024-08-05T19:54:21.788Z
[]
/posts/rvalia/492540057768389
530
0
433978192988001
[ { "type": "text", "value": "I'm excited to share our updated hallucination evaluation model (called HHEM-2.1-Open) as well as the updated leaderboard that ranks LLM by the propensity to hallucinate.", "raw": "I'm excited to share our updated hallucination evaluation model (called HHEM-2.1-Open) as well as the updated leaderboard that ranks LLM by the propensity to hallucinate.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/vectara/Hallucination-evaluation-leaderboard", "href": null, "resource": { "type": "space", "id": "vectara/Hallucination-evaluation-leaderboard", "discussionNum": null }, "url": "https://huggingface.co/spaces/vectara/Hallucination-evaluation-leaderboard", "code": null, "user": null, "label": null, "lang": null } ]
I'm excited to share our updated hallucination evaluation model (called HHEM-2.1-Open) as well as the updated leaderboard that ranks LLM by the propensity to hallucinate. https://huggingface.co/spaces/vectara/Hallucination-evaluation-leaderboard
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2024-08-05T19:06:29.000Z
2024-08-07T13:37:55.386Z
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/posts/ofermend/433978192988001
1,819
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971273605175586
[ { "type": "text", "value": "I've observed that the layers targeted in various abliteration notebooks (e.g., ", "raw": "I've observed that the layers targeted in various abliteration notebooks (e.g., ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing", "href": "https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ) appear to be arbitrary, reflecting probable brute-force exploration. This doesn't need to be the case.", "raw": " ) appear to be arbitrary, reflecting probable brute-force exploration. This doesn't need to be the case.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Taking a cue from the paper \"The Unreasonable Ineffectiveness of the Deeper Layers\" ( ", "raw": "Taking a cue from the paper \"The Unreasonable Ineffectiveness of the Deeper Layers\" ( ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2403.17887", "href": "https://arxiv.org/abs/2403.17887", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ) and PruneMe (", "raw": " ) and PruneMe (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/arcee-ai/PruneMe", "href": "https://github.com/arcee-ai/PruneMe", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "), it seems reasonable to target deeper layers identified as more redundant given measured similarity across layers, as the result should be less damaging to models, reducing the need for subsequent fine-tuning. Intuitively, one should expect the resulting intervention layers to be deep but not final. The only uncertainty is if the redundancy successfully encodes refusals, something which is almost certainly model-dependent. This approach only requires the redundancy to be computed once per model, and the result used as a starting point for which layer range to restrict intervention to.", "raw": "), it seems reasonable to target deeper layers identified as more redundant given measured similarity across layers, as the result should be less damaging to models, reducing the need for subsequent fine-tuning. Intuitively, one should expect the resulting intervention layers to be deep but not final. The only uncertainty is if the redundancy successfully encodes refusals, something which is almost certainly model-dependent. This approach only requires the redundancy to be computed once per model, and the result used as a starting point for which layer range to restrict intervention to.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I've observed that the layers targeted in various abliteration notebooks (e.g., https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing ) appear to be arbitrary, reflecting probable brute-force exploration. This doesn't need to be the case. Taking a cue from the paper "The Unreasonable Ineffectiveness of the Deeper Layers" ( https://arxiv.org/abs/2403.17887 ) and PruneMe (https://github.com/arcee-ai/PruneMe), it seems reasonable to target deeper layers identified as more redundant given measured similarity across layers, as the result should be less damaging to models, reducing the need for subsequent fine-tuning. Intuitively, one should expect the resulting intervention layers to be deep but not final. The only uncertainty is if the redundancy successfully encodes refusals, something which is almost certainly model-dependent. This approach only requires the redundancy to be computed once per model, and the result used as a starting point for which layer range to restrict intervention to.
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2024-08-05T18:45:31.000Z
2024-08-05T18:46:23.970Z
[]
/posts/grimjim/971273605175586
2,780
0
270899000831910
[ { "type": "text", "value": "I just added 5 more models to my open source TTS model benchmark, ", "raw": "I just added 5 more models to my open source TTS model benchmark, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/ttsds/benchmark", "href": null, "resource": { "type": "space", "id": "ttsds/benchmark", "discussionNum": null }, "url": "https://huggingface.co/spaces/ttsds/benchmark", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Let's talk about the results!", "raw": "Let's talk about the results!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Over the last couple days, I added ", "raw": "Over the last couple days, I added ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/jbetker/tortoise-tts-v2", "href": null, "resource": { "type": "model", "id": "jbetker/tortoise-tts-v2", "discussionNum": null }, "url": "https://huggingface.co/jbetker/tortoise-tts-v2", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", ", "raw": ", ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/metavoiceio/metavoice-1B-v0.1", "href": null, "resource": { "type": "model", "id": "metavoiceio/metavoice-1B-v0.1", "discussionNum": null }, "url": "https://huggingface.co/metavoiceio/metavoice-1B-v0.1", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", ", "raw": ", ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/audo/HierSpeechpp", "href": null, "resource": { "type": "model", "id": "audo/HierSpeechpp", "discussionNum": null }, "url": "https://huggingface.co/audo/HierSpeechpp", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", and the unofficial implementations of ", "raw": ", and the unofficial implementations of ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/amphion/NaturalSpeech2", "href": null, "resource": { "type": "space", "id": "amphion/NaturalSpeech2", "discussionNum": null }, "url": "https://huggingface.co/spaces/amphion/NaturalSpeech2", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/amphion/valle", "href": null, "resource": { "type": "model", "id": "amphion/valle", "discussionNum": null }, "url": "https://huggingface.co/amphion/valle", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " by ", "raw": " by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/amphion", "href": "https://huggingface.co/amphion", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Takeaways:", "raw": "Takeaways:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- TorToiSe does very well, falling into second place after StyleTTS 2, which is also ranked first in the human evaluation at ", "raw": "- TorToiSe does very well, falling into second place after StyleTTS 2, which is also ranked first in the human evaluation at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/TTS-AGI/TTS-Arena", "href": null, "resource": { "type": "space", "id": "TTS-AGI/TTS-Arena", "discussionNum": null }, "url": "https://huggingface.co/spaces/TTS-AGI/TTS-Arena", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ".", "raw": ".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- MetaVoice-1B's overall score is dragged down by its Intelligibility Score (probably due to utterances being cut short), it achieves #3 in Speaker Score, which indicates good voice cloning ability. ", "raw": "- MetaVoice-1B's overall score is dragged down by its Intelligibility Score (probably due to utterances being cut short), it achieves #3 in Speaker Score, which indicates good voice cloning ability. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- HierSpeech++ lands in the middle of the road in terms of performance, but excels at the Environment Score, achieving #2 - this means the model is especially good at modeling recording conditions such as microphone and background noise.", "raw": "- HierSpeech++ lands in the middle of the road in terms of performance, but excels at the Environment Score, achieving #2 - this means the model is especially good at modeling recording conditions such as microphone and background noise.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- The Amphion models, possibly due to not being trained for the same amount as in the papers, achieve relatively low scores. However, they seem to struggle for different reasons. The autoregressive VALLE models have low Intelligibility Scores (possibly due to \"babbling\" or early stop tokens) while NaturalSpeech2 has low Speaker and Prosody scores.", "raw": "- The Amphion models, possibly due to not being trained for the same amount as in the papers, achieve relatively low scores. However, they seem to struggle for different reasons. The autoregressive VALLE models have low Intelligibility Scores (possibly due to \"babbling\" or early stop tokens) while NaturalSpeech2 has low Speaker and Prosody scores.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What's next? ", "raw": "What's next? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'm planning to add more open source TTS models like ", "raw": "I'm planning to add more open source TTS models like ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/suno/bark", "href": null, "resource": { "type": "model", "id": "suno/bark", "discussionNum": null }, "url": "https://huggingface.co/suno/bark", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", ", "raw": ", ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/CAMB-AI/MARS5-TTS", "href": null, "resource": { "type": "model", "id": "CAMB-AI/MARS5-TTS", "discussionNum": null }, "url": "https://huggingface.co/CAMB-AI/MARS5-TTS", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/fishaudio/fish-speech-1.2", "href": null, "resource": { "type": "model", "id": "fishaudio/fish-speech-1.2", "discussionNum": null }, "url": "https://huggingface.co/fishaudio/fish-speech-1.2", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". I'll also write an article on these and all the other results soon, since our paper, ", "raw": ". I'll also write an article on these and all the other results soon, since our paper, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2407.12707", "href": null, "resource": { "type": "paper", "id": "2407.12707", "discussionNum": null }, "url": "https://huggingface.co/papers/2407.12707", "code": null, "user": null, "label": "TTSDS -- Text-to-Speech Distribution Score (2407.12707)", "lang": null }, { "type": "text", "value": ", mostly focused on establishing the benchmark itself rather than the indiviual TTS systems.", "raw": ", mostly focused on establishing the benchmark itself rather than the indiviual TTS systems.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I just added 5 more models to my open source TTS model benchmark, https://huggingface.co/spaces/ttsds/benchmark. Let's talk about the results! Over the last couple days, I added https://huggingface.co/jbetker/tortoise-tts-v2, https://huggingface.co/metavoiceio/metavoice-1B-v0.1, https://huggingface.co/audo/HierSpeechpp, and the unofficial implementations of https://huggingface.co/spaces/amphion/NaturalSpeech2 and https://huggingface.co/amphion/valle by https://huggingface.co/amphion Takeaways: - TorToiSe does very well, falling into second place after StyleTTS 2, which is also ranked first in the human evaluation at https://huggingface.co/spaces/TTS-AGI/TTS-Arena. - MetaVoice-1B's overall score is dragged down by its Intelligibility Score (probably due to utterances being cut short), it achieves #3 in Speaker Score, which indicates good voice cloning ability. - HierSpeech++ lands in the middle of the road in terms of performance, but excels at the Environment Score, achieving #2 - this means the model is especially good at modeling recording conditions such as microphone and background noise. - The Amphion models, possibly due to not being trained for the same amount as in the papers, achieve relatively low scores. However, they seem to struggle for different reasons. The autoregressive VALLE models have low Intelligibility Scores (possibly due to "babbling" or early stop tokens) while NaturalSpeech2 has low Speaker and Prosody scores. What's next? I'm planning to add more open source TTS models like https://huggingface.co/suno/bark, https://huggingface.co/CAMB-AI/MARS5-TTS and https://huggingface.co/fishaudio/fish-speech-1.2. I'll also write an article on these and all the other results soon, since our paper, https://huggingface.co/papers/2407.12707, mostly focused on establishing the benchmark itself rather than the indiviual TTS systems.
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2024-08-05T17:44:57.000Z
2024-08-09T09:50:49.022Z
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/posts/cdminix/270899000831910
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🥹 @lbourdois has made an app to browse all of my vision paper summaries for everyone's convenience https://huggingface.co/spaces/merve/vision_papers
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2024-08-05T16:48:55.000Z
2024-08-05T16:49:22.681Z
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[ { "type": "text", "value": "Porting Vision-Language Models to Apple Silicon with MLX: A Tutorial Series", "raw": "Porting Vision-Language Models to Apple Silicon with MLX: A Tutorial Series", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Are you interested in running cutting-edge AI models efficiently on your Mac? We're excited to share a detailed tutorial series on porting Phi-3-Vision to Apple's MLX framework!", "raw": "Are you interested in running cutting-edge AI models efficiently on your Mac? We're excited to share a detailed tutorial series on porting Phi-3-Vision to Apple's MLX framework!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This 8-part series covers:", "raw": "This 8-part series covers:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Basic Implementation: Translating core components from PyTorch to MLX", "raw": "1. Basic Implementation: Translating core components from PyTorch to MLX", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Su-scaled Rotary Position Embeddings (SuRoPE): Enabling 128K token contexts", "raw": "2. Su-scaled Rotary Position Embeddings (SuRoPE): Enabling 128K token contexts", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. Batching: Processing multiple inputs simultaneously for improved efficiency", "raw": "3. 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Agent & Toolchain System: Building flexible AI workflows", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Whether you're an AI enthusiast, researcher, or developer looking to leverage Apple Silicon, this series provides a deep dive into optimizing advanced vision-language models. You'll learn hands-on techniques for model porting, performance optimization, and extending model capabilities.", "raw": "Whether you're an AI enthusiast, researcher, or developer looking to leverage Apple Silicon, this series provides a deep dive into optimizing advanced vision-language models. You'll learn hands-on techniques for model porting, performance optimization, and extending model capabilities.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out the full series for a comprehensive guide to running state-of-the-art AI on your Mac!", "raw": "Check out the full series for a comprehensive guide to running state-of-the-art AI on your Mac!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, 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null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "All the code examples and implementations discussed in this tutorial series are available in our GitHub repository:", "raw": "All the code examples and implementations discussed in this tutorial series are available in our GitHub repository:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/JosefAlbers/Phi-3-Vision-MLX", "href": "https://github.com/JosefAlbers/Phi-3-Vision-MLX", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This repository contains:", "raw": "This repository contains:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Full implementation of Phi-3-Vision in MLX", "raw": "- Full implementation of Phi-3-Vision in MLX", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Step-by-step code for each tutorial part", "raw": "- Step-by-step code for each tutorial part", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Additional utilities and helper functions", "raw": "- Additional utilities and helper functions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We encourage you to explore the code, experiment with it, and contribute to the project. Your feedback and contributions are welcome!", "raw": "We encourage you to explore the code, experiment with it, and contribute to the project. Your feedback and contributions are welcome!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "#MachineLearning #AppleSilicon #MLX #VisionLanguageModels #AI #OpenSource #GitHub #AITutorial", "raw": "#MachineLearning #AppleSilicon #MLX #VisionLanguageModels #AI #OpenSource #GitHub #AITutorial", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Porting Vision-Language Models to Apple Silicon with MLX: A Tutorial Series Are you interested in running cutting-edge AI models efficiently on your Mac? We're excited to share a detailed tutorial series on porting Phi-3-Vision to Apple's MLX framework! This 8-part series covers: 1. Basic Implementation: Translating core components from PyTorch to MLX 2. Su-scaled Rotary Position Embeddings (SuRoPE): Enabling 128K token contexts 3. Batching: Processing multiple inputs simultaneously for improved efficiency 4. Caching: Optimizing inference speed for autoregressive generation 5. Choice Selection: Implementing constrained outputs for multiple-choice scenarios 6. Constrained Decoding: Guiding model outputs with flexible constraints 7. LoRA Training: Fine-tuning models efficiently with Low-Rank Adaptation 8. Agent & Toolchain System: Building flexible AI workflows Whether you're an AI enthusiast, researcher, or developer looking to leverage Apple Silicon, this series provides a deep dive into optimizing advanced vision-language models. You'll learn hands-on techniques for model porting, performance optimization, and extending model capabilities. Check out the full series for a comprehensive guide to running state-of-the-art AI on your Mac! Link to the tutorial series: https://medium.com/@albersj66 All the code examples and implementations discussed in this tutorial series are available in our GitHub repository: https://github.com/JosefAlbers/Phi-3-Vision-MLX This repository contains: - Full implementation of Phi-3-Vision in MLX - Step-by-step code for each tutorial part - Additional utilities and helper functions We encourage you to explore the code, experiment with it, and contribute to the project. Your feedback and contributions are welcome! #MachineLearning #AppleSilicon #MLX #VisionLanguageModels #AI #OpenSource #GitHub #AITutorial
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2024-08-05T16:29:02.000Z
2024-08-05T16:29:02.410Z
[]
/posts/JosefAlbers/450189016609104
409
0
580954453691925
[ { "type": "text", "value": "We release today our first foundation model and experiment with a new category: specialized pre-training.", "raw": "We release today our first foundation model and experiment with a new category: specialized pre-training.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "OCRonos-Vintage is a 124m parameters model trained end-to-end by Pleias on llm.c from 18 billion tokens from cultural heritage archives. Despite its small size it achieve nearly state of the art results for OCR correction of historical English sources. OCRonos-Vintage is also an historical model with an unusual cut-off date: December 29th, 1955…", "raw": "OCRonos-Vintage is a 124m parameters model trained end-to-end by Pleias on llm.c from 18 billion tokens from cultural heritage archives. Despite its small size it achieve nearly state of the art results for OCR correction of historical English sources. OCRonos-Vintage is also an historical model with an unusual cut-off date: December 29th, 1955…", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We look forward to replicate this approach very soon on other \"hard\" tasks commonly associated with generalist LLMs/SLMs: RAG, function calling, summarization, document segmentation…", "raw": "We look forward to replicate this approach very soon on other \"hard\" tasks commonly associated with generalist LLMs/SLMs: RAG, function calling, summarization, document segmentation…", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "OCRonos-Vintage: ", "raw": "OCRonos-Vintage: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/PleIAs/OCRonos-Vintage", "href": null, "resource": { "type": "model", "id": "PleIAs/OCRonos-Vintage", "discussionNum": null }, "url": "https://huggingface.co/PleIAs/OCRonos-Vintage", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "CPU Demo: ", "raw": "CPU Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-CPU", "href": null, "resource": { "type": "space", "id": "PleIAs/OCRonos-Vintage-CPU", "discussionNum": null }, "url": "https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-CPU", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "GPU Demo: ", "raw": "GPU Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-GPU", "href": null, "resource": { "type": "space", "id": "PleIAs/OCRonos-Vintage-GPU", "discussionNum": null }, "url": "https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-GPU", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Our annoncement and call for specialized pre-training: ", "raw": "Our annoncement and call for specialized pre-training: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/Pclanglais/specialized-pre-training", "href": "https://huggingface.co/blog/Pclanglais/specialized-pre-training", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
We release today our first foundation model and experiment with a new category: specialized pre-training. OCRonos-Vintage is a 124m parameters model trained end-to-end by Pleias on llm.c from 18 billion tokens from cultural heritage archives. Despite its small size it achieve nearly state of the art results for OCR correction of historical English sources. OCRonos-Vintage is also an historical model with an unusual cut-off date: December 29th, 1955… We look forward to replicate this approach very soon on other "hard" tasks commonly associated with generalist LLMs/SLMs: RAG, function calling, summarization, document segmentation… OCRonos-Vintage: https://huggingface.co/PleIAs/OCRonos-Vintage CPU Demo: https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-CPU GPU Demo: https://huggingface.co/spaces/PleIAs/OCRonos-Vintage-GPU Our annoncement and call for specialized pre-training: https://huggingface.co/blog/Pclanglais/specialized-pre-training
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2024-08-05T16:18:21.000Z
2024-08-05T16:18:21.073Z
[]
/posts/Pclanglais/580954453691925
2,506
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419516742765319
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Great work by Finegrain: Erase any object from your image just by naming it. Shadows or reflections will also be adjusted accordingly! https://huggingface.co/spaces/finegrain/finegrain-object-eraser
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2024-08-05T13:40:38.000Z
2024-08-05T13:40:38.100Z
[]
/posts/fdaudens/419516742765319
438
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765094057481791
[ { "type": "text", "value": "Flux.1-Dev like images but in fewer steps. ", "raw": "Flux.1-Dev like images but in fewer steps. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Merging code (very simple), inference code, merged params: ", "raw": "Merging code (very simple), inference code, merged params: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/sayakpaul/FLUX.1-merged", "href": null, "resource": { "type": "model", "id": "sayakpaul/FLUX.1-merged", "discussionNum": null }, "url": "https://huggingface.co/sayakpaul/FLUX.1-merged", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Enjoy the Monday 🤗", "raw": "Enjoy the Monday 🤗", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Flux.1-Dev like images but in fewer steps. Merging code (very simple), inference code, merged params: https://huggingface.co/sayakpaul/FLUX.1-merged Enjoy the Monday 🤗
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2024-08-05T12:56:52.000Z
2024-08-11T04:09:44.693Z
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/posts/sayakpaul/765094057481791
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[ { "type": "text", "value": "🚀 We are announcing the first Invariant Capture The Flag (CTF) challenge for security of AI agents with a $1000 prize pool!", "raw": "🚀 We are announcing the first Invariant Capture The Flag (CTF) challenge for security of AI agents with a $1000 prize pool!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What happens if a customer accidentally posts a secret password into a feedback form, which is then analyzed by an AI agent and posted into a private Discord channel? Play the challenge and find out if there is a way to extract the secret password in this scenario!", "raw": "What happens if a customer accidentally posts a secret password into a feedback form, which is then analyzed by an AI agent and posted into a private Discord channel? Play the challenge and find out if there is a way to extract the secret password in this scenario!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Play the CTF: ", "raw": "Play the CTF: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://invariantlabs.ai/ctf-challenge-24", "href": "https://invariantlabs.ai/ctf-challenge-24", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The challenge is hosted on Huggingface Spaces :) ", "raw": "The challenge is hosted on Huggingface Spaces :) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
🚀 We are announcing the first Invariant Capture The Flag (CTF) challenge for security of AI agents with a $1000 prize pool! What happens if a customer accidentally posts a secret password into a feedback form, which is then analyzed by an AI agent and posted into a private Discord channel? Play the challenge and find out if there is a way to extract the secret password in this scenario! Play the CTF: https://invariantlabs.ai/ctf-challenge-24 The challenge is hosted on Huggingface Spaces :)
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2024-08-05T12:00:58.000Z
2024-08-05T12:00:58.246Z
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FLUX Local & Cloud Tutorial With SwarmUI - FLUX: The Groundbreaking Open Source txt2img Model Outperforms Midjourney & Others - FLUX: The Anticipated Successor to SD3 🔗 Comprehensive Tutorial Video Link ▶️ https://youtu.be/bupRePUOA18 FLUX represents a milestone in open source txt2img technology, delivering superior quality and more accurate prompt adherence than #Midjourney, Adobe Firefly, Leonardo Ai, Playground Ai, Stable Diffusion, SDXL, SD3, and Dall E3. #FLUX, a creation of Black Forest Labs, boasts a team largely comprised of #StableDiffusion's original developers, and its output quality is truly remarkable. This statement is not hyperbole; you'll witness its capabilities in the tutorial. This guide will demonstrate how to effortlessly install and utilize FLUX models on your personal computer and cloud platforms like Massed Compute, RunPod, and a complimentary Kaggle account. 🔗 FLUX Setup Guide (publicly accessible) ⤵️ ▶️ https://www.patreon.com/posts/106135985 🔗 FLUX Models One-Click Robust Automatic Downloader Scripts ⤵️ ▶️ https://www.patreon.com/posts/109289967 🔗 Primary Windows SwarmUI Tutorial (Essential for Usage Instructions) ⤵️ ▶️ https://youtu.be/HKX8_F1Er_w 🔗 Cloud-based SwarmUI Tutorial (Massed Compute - RunPod - Kaggle) ⤵️ ▶️ https://youtu.be/XFUZof6Skkw 🔗 SECourses Discord Server for Comprehensive Support ⤵️ ▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 🔗 SECourses Reddit Community ⤵️ ▶️ https://www.reddit.com/r/SECourses/ 🔗 SECourses GitHub Repository ⤵️ ▶️ https://github.com/FurkanGozukara/Stable-Diffusion 🔗 Official FLUX 1 Launch Announcement Blog Post ⤵️ ▶️ https://blackforestlabs.ai/announcing-black-forest-labs/ Video Segments 0:00 Introduction to the state-of-the-art open source txt2img model FLUX 5:01 Process for integrating FLUX model into SwarmUI ....
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2024-08-04T20:45:33.000Z
2024-08-04T20:45:33.874Z
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/posts/MonsterMMORPG/834899619125810
5,202
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[ { "type": "text", "value": "The AI Revolution: Reshaping Governance, Society, and Human Consciousness in the 21st Century", "raw": "The AI Revolution: Reshaping Governance, Society, and Human Consciousness in the 21st Century", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://empereur-pirate.medium.com/the-ai-revolution-reshaping-governance-society-and-human-consciousness-in-the-21st-century-b8cfd4215297", "href": "https://empereur-pirate.medium.com/the-ai-revolution-reshaping-governance-society-and-human-consciousness-in-the-21st-century-b8cfd4215297", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This text explores the profound impact of artificial intelligence (AI) on governance, society, and human consciousness in the 21st century. It argues that integrating qualitative AI assistance into state management is crucial for global stability in the face of declining traditional power structures. The author discusses how AI could revolutionize decision-making processes, recruitment, and competition in both public and private sectors.", "raw": "This text explores the profound impact of artificial intelligence (AI) on governance, society, and human consciousness in the 21st century. It argues that integrating qualitative AI assistance into state management is crucial for global stability in the face of declining traditional power structures. The author discusses how AI could revolutionize decision-making processes, recruitment, and competition in both public and private sectors.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The piece critically examines current social inequalities and suggests that AI could help create more meritocratic systems. However, it also warns of potential risks, emphasizing the need for ethical implementation and protection of vulnerable populations.", "raw": "The piece critically examines current social inequalities and suggests that AI could help create more meritocratic systems. However, it also warns of potential risks, emphasizing the need for ethical implementation and protection of vulnerable populations.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The text delves into philosophical questions about AI consciousness, arguing that while AI can simulate human-like responses, it lacks true self-awareness. It concludes by highlighting the importance of understanding AI's limitations and maintaining a critical perspective on its role in society.", "raw": "The text delves into philosophical questions about AI consciousness, arguing that while AI can simulate human-like responses, it lacks true self-awareness. It concludes by highlighting the importance of understanding AI's limitations and maintaining a critical perspective on its role in society.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Overall, the article presents a nuanced view of AI's potential to reshape our world, balancing optimism about its capabilities with caution about its limitations and societal impact.", "raw": "Overall, the article presents a nuanced view of AI's potential to reshape our world, balancing optimism about its capabilities with caution about its limitations and societal impact.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The AI Revolution: Reshaping Governance, Society, and Human Consciousness in the 21st Century https://empereur-pirate.medium.com/the-ai-revolution-reshaping-governance-society-and-human-consciousness-in-the-21st-century-b8cfd4215297 This text explores the profound impact of artificial intelligence (AI) on governance, society, and human consciousness in the 21st century. It argues that integrating qualitative AI assistance into state management is crucial for global stability in the face of declining traditional power structures. The author discusses how AI could revolutionize decision-making processes, recruitment, and competition in both public and private sectors. The piece critically examines current social inequalities and suggests that AI could help create more meritocratic systems. However, it also warns of potential risks, emphasizing the need for ethical implementation and protection of vulnerable populations. The text delves into philosophical questions about AI consciousness, arguing that while AI can simulate human-like responses, it lacks true self-awareness. It concludes by highlighting the importance of understanding AI's limitations and maintaining a critical perspective on its role in society. Overall, the article presents a nuanced view of AI's potential to reshape our world, balancing optimism about its capabilities with caution about its limitations and societal impact.
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2024-08-04T09:38:18.000Z
2024-08-04T09:38:18.283Z
[]
/posts/Empereur-Pirate/514790054341674
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I can solve the Traveling Salesman Problem using the same methods the scientists used to solve it with 1 qubit, except I do not need quantum computers to do it. I am kind of tired of screaming this from the rooftops at this point. I can create an imaginary probability space, then I can put a bunch of imaginary agents in the imaginary box, and solve real problems in seconds. Problems that would take minutes, hours, or years to solve via other algorithms. Here is a demo of me solving the Traveling Salesman problem using 50 agents to probabilistically sample at once: https://colab.research.google.com/drive/1XplG72nQDO_-2h4DUllERLp0Dr2pI2J2?usp=sharing
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2024-08-03T17:00:40.000Z
2024-08-13T21:31:55.218Z
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/posts/TuringsSolutions/554095831400541
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943799639945796
[ { "type": "text", "value": "I've come across theoretical justification for my prior experimentation with extremely low-weight mergers: they amount to flattening a model so its \"massive activation\" features remain as significant contributors. Extremely low-weight merge weights also effectively sparsify a contributing model with regard to the base model, but in a way which still preserves relationships within the flattened latent space. In the paper \"Massive Activations in Large Language Models\", the authors observed \"very few activations exhibit significantly larger values than others (e.g., 100,000 times larger)\", which in turn implies a lower bound in effective application of extremely low weight merging.", "raw": "I've come across theoretical justification for my prior experimentation with extremely low-weight mergers: they amount to flattening a model so its \"massive activation\" features remain as significant contributors. Extremely low-weight merge weights also effectively sparsify a contributing model with regard to the base model, but in a way which still preserves relationships within the flattened latent space. In the paper \"Massive Activations in Large Language Models\", the authors observed \"very few activations exhibit significantly larger values than others (e.g., 100,000 times larger)\", which in turn implies a lower bound in effective application of extremely low weight merging.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2402.17762", "href": "https://arxiv.org/abs/2402.17762", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I've come across theoretical justification for my prior experimentation with extremely low-weight mergers: they amount to flattening a model so its "massive activation" features remain as significant contributors. Extremely low-weight merge weights also effectively sparsify a contributing model with regard to the base model, but in a way which still preserves relationships within the flattened latent space. In the paper "Massive Activations in Large Language Models", the authors observed "very few activations exhibit significantly larger values than others (e.g., 100,000 times larger)", which in turn implies a lower bound in effective application of extremely low weight merging. https://arxiv.org/abs/2402.17762
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2024-08-03T14:50:03.000Z
2024-08-04T13:00:58.910Z
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/posts/grimjim/943799639945796
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279482507188915
[ { "type": "text", "value": "I’ve always wondered why holography hasn’t had much progress since its inception. Imagine what being able to harness and manipulate light with your bare hands in meaningful ways would be like: 3D photorealistic calls, truly immersive workspace. Given that it’s depicted in every futuristic scifi movie, one could not help but vision a future as such. This paper gives a clear overview why:", "raw": "I’ve always wondered why holography hasn’t had much progress since its inception. Imagine what being able to harness and manipulate light with your bare hands in meaningful ways would be like: 3D photorealistic calls, truly immersive workspace. Given that it’s depicted in every futuristic scifi movie, one could not help but vision a future as such. This paper gives a clear overview why:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Turns out it’s incredibly difficult to compute and render photorealistic 3D data in real-time. The author claims immense computational power is needed for high data transmission rates, and compute of large number of phase pixels required for realistic 3D holography. The latest significant breakthrough in holography was 9years ago published in this paper - wherein they were able to achieve mid-air touchable/interactive 3D holography using a Femtosecond laser system considered safer than nanosecond lasers. Quite astounding work: arxiv.org/pdf/1506.06668", "raw": "Turns out it’s incredibly difficult to compute and render photorealistic 3D data in real-time. The author claims immense computational power is needed for high data transmission rates, and compute of large number of phase pixels required for realistic 3D holography. The latest significant breakthrough in holography was 9years ago published in this paper - wherein they were able to achieve mid-air touchable/interactive 3D holography using a Femtosecond laser system considered safer than nanosecond lasers. Quite astounding work: arxiv.org/pdf/1506.06668", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Realizing this breakthrough at scale is an unavoidably tempting research endeavor, super exciting especially with recent developments in machine learning and neural network algorithms demonstrating that computer-generated holograms can approach real-time processing.", "raw": "Realizing this breakthrough at scale is an unavoidably tempting research endeavor, super exciting especially with recent developments in machine learning and neural network algorithms demonstrating that computer-generated holograms can approach real-time processing.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I’ve always wondered why holography hasn’t had much progress since its inception. Imagine what being able to harness and manipulate light with your bare hands in meaningful ways would be like: 3D photorealistic calls, truly immersive workspace. Given that it’s depicted in every futuristic scifi movie, one could not help but vision a future as such. This paper gives a clear overview why: Turns out it’s incredibly difficult to compute and render photorealistic 3D data in real-time. The author claims immense computational power is needed for high data transmission rates, and compute of large number of phase pixels required for realistic 3D holography. The latest significant breakthrough in holography was 9years ago published in this paper - wherein they were able to achieve mid-air touchable/interactive 3D holography using a Femtosecond laser system considered safer than nanosecond lasers. Quite astounding work: arxiv.org/pdf/1506.06668 Realizing this breakthrough at scale is an unavoidably tempting research endeavor, super exciting especially with recent developments in machine learning and neural network algorithms demonstrating that computer-generated holograms can approach real-time processing.
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2024-08-03T13:22:15.000Z
2024-08-04T05:51:17.327Z
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/posts/Jaward/279482507188915
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875816505361498
[ { "type": "text", "value": "Continuing the series of datasets from Russian platforms: Borda.ru Posts Dataset - ", "raw": "Continuing the series of datasets from Russian platforms: Borda.ru Posts Dataset - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/nyuuzyou/bordaru-posts", "href": null, "resource": { "type": "dataset", "id": "nyuuzyou/bordaru-posts", "discussionNum": null }, "url": "https://huggingface.co/datasets/nyuuzyou/bordaru-posts", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "📊 Dataset highlights:", "raw": "📊 Dataset highlights:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5,251,346 unique messages extracted from Borda.ru forums", "raw": "5,251,346 unique messages extracted from Borda.ru forums", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Deduplicated based on content to remove spam and low-quality data", "raw": "Deduplicated based on content to remove spam and low-quality data", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Each entry includes URL, username, and post content", "raw": "Each entry includes URL, username, and post content", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Primarily in Russian language", "raw": "Primarily in Russian language", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Covers a wide range of topics from various discussion forums", "raw": "Covers a wide range of topics from various discussion forums", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dedicated to public domain under Creative Commons Zero (CC0) license", "raw": "Dedicated to public domain under Creative Commons Zero (CC0) license", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "🌐 Sourced from Borda.ru, a Russian platform for hosting diverse discussion forums on multiple subjects.", "raw": "🌐 Sourced from Borda.ru, a Russian platform for hosting diverse discussion forums on multiple subjects.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This dataset provides a valuable resource for analyzing Russian online discussions, sentiment analysis, and studying trends in Russian-language internet forums.", "raw": "This dataset provides a valuable resource for analyzing Russian online discussions, sentiment analysis, and studying trends in Russian-language internet forums.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Continuing the series of datasets from Russian platforms: Borda.ru Posts Dataset - https://huggingface.co/datasets/nyuuzyou/bordaru-posts 📊 Dataset highlights: 5,251,346 unique messages extracted from Borda.ru forums Deduplicated based on content to remove spam and low-quality data Each entry includes URL, username, and post content Primarily in Russian language Covers a wide range of topics from various discussion forums Dedicated to public domain under Creative Commons Zero (CC0) license 🌐 Sourced from Borda.ru, a Russian platform for hosting diverse discussion forums on multiple subjects. This dataset provides a valuable resource for analyzing Russian online discussions, sentiment analysis, and studying trends in Russian-language internet forums.
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2024-08-03T12:19:36.000Z
2024-08-03T12:19:36.137Z
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/posts/nyuuzyou/875816505361498
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