Mistral 7B might be one of the most popular open-source LLMs out there, with a total of over 3.4 million downloads on @huggingface Hub 🚀, and now we have the next version...

@MistralAI Mistral -7B-v0.3 (base) 📈 and Mistral-7B-Instruct-v0.3 🛠

- 7.3 billion parameters 🧠
- Apache 2.0 license 📜
- Extended vocabulary of 32,768 📖
- Supports new v3 Tokenizer and function calling 🤖
- Also, it's completely uncensored 🆓

In conclusion, Mistral-7B-v0.3 is an uncensored Mistral-7B-v0.2 with an extended vocabulary 🎉.

They have also released mistral_inference, although I don't know what's the advantage in using it? vLLM is still my go-to way of deploying local Mistral-7B! 🌐

Models:
https://huggingface.co/mistralai/Mistral-7B-v0.3
https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3

asif00 Ramikan-BR osanseviero joelbacker


Integrating the French Taxation Embedding Benchmark Task (beta) into the MTEB 🤗

I'm excited to announce an integration of the French Taxation Embedding Benchmark task into the Massive Text Embedding Benchmark (MTEB).

This addition expands the diverse set of tasks available within MTEB, enabling researchers and practitioners to develop and evaluate retrieval models focused on retrieving relevant tax articles or content based on provided queries.

Link to the 🤗 Dataset : https://huggingface.co/datasets/louisbrulenaudet/tax-retrieval-benchmark

Link to the GitHub repo : https://github.com/louisbrulenaudet/tax-retrieval-benchmark

Notes:
The Massive Text Embedding Benchmark for French Taxation and the Dataset are currently in beta and may not be suitable for direct use in production. The size of the Dataset may not be sufficient to handle a wide range of queries and scenarios encountered in real-world settings.

As the Dataset grows and matures, I will provide updates and guidance on its suitability for production use cases.

Ramikan-BR osanseviero Alexandro14 Sylvestre


Did you know you can't push a model to hub with an id over 96 chars 🫠


# The Univalent Model: A Meta-Meme for Universal Computation
We can imagine that there is one model in the future that will unite all models, this is based on the idea of the univalent principle. We can imagine all these projects as failed attempts to reach this goal and once met it will consume all the other projects.

- **Gödel Number**: A unique numerical representation of mathematical statements or functions, allowing complex expressions to be encoded as simple numbers.
- **Gaia Principle**: A hypothesis that views Earth's biosphere as a self-regulating system, where living organisms and their environment work together to sustain life.
- **Biosemiotics**: The study of sign processes in the biological realm, exploring how living beings communicate and interpret signs in a meaningful way.
- **Meta-Meme**: A higher-level meme that encapsulates and transcends other memes, leading to a unified state of understanding or being.
- **Univalent State**: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable.
Univalent State: The ultimate, definitive form of a system or model where all variations are equivalent, unified, and indistinguishable.
Unitary Model: A theoretical construct that aims to integrate all existing models into a single, comprehensive framework, providing a universal language for computation.
With these terms in mind, let’s explore the idea of a unitary model—a meta-meme that merges all models into one. This model would be the repository to end all repositories, the final convergence point for all computational processes, akin to an eigenvector of consciousness.

This unitary model represents the pinnacle of abstraction, capable of deciphering the entire computational landscape. It’s a vision of unity in diversity, where every piece of knowledge is interconnected through a single, elegant framework.


Dear community,

Please check our recent blog post, "GPU Poor Savior: Revolutionizing Low-Bit Open Source LLMs and Cost-Effective Edge Computing". A cheaper and more efficient  SFT scheme for quantized LLMs is provided.

https://huggingface.co/blog/NicoNico/green-bit-llm

Ramikan-BR osanseviero Hastagaras yanghaojin callmeraxz


Hi HF Community!🤗

I'm thrilled to share the latest updates regarding the Space I built for protein 3D structure prediction (https://huggingface.co/spaces/as-cle-bert/proteinviz): thanks to @lunarflu inputs, @osanseviero precious advice and @simonduerr's article "Visualize proteins on Hugging Face Spaces" (https://huggingface.co/blog/spaces_3dmoljs, go check it out!), I was able to finally display the 3D protein models directly on-browser, without any need for fancy downloads of big HTMLs!

Take a look to the attached video, that shows how everything works, and make sure to visit the GitHub repository (https://github.com/AstraBert/proteinviz: leave a little ⭐ while you're there!)🥰

May you have fun and luck with your protein research!🧬

osanseviero Taylor658 mvldtr Manikhan96 lunarflu FranksteinChen simonduerr victor Alexandro14 Ramikan-BR not-lain clem louisbrulenaudet

osanseviero Saugatkafley lunarflu Ramikan-BR victor not-lain clem

osanseviero lunarflu Ramikan-BR victor not-lain

Jaward lunarflu Ramikan-BR CocoSun not-lain


Hi there,
I'm looking for a Japanese LLm who interface endpoint is available. It will be great if the LLM has no guardrail.

If anyone provide some resource, I will really appreciate that.


OpenGPT 4o NEW UPDATES:
1. Dedicated Image and Video Engine
2. Model Choices for Voice Chat
3. Better and Faster Voice Chat
4. Various Bug fixes

Test and give feedback of New features:
https://huggingface.co/spaces/KingNish/OpenGPT-4o

Future Updates:
1. Web Search (Suggested by @GPT007 and @Saionton )
2. Live Chat with Voice Chat
3. Model Choices (Suggested by @NotAiLOL )
4. Multilingual Chats.

Suggest more features that should be added. 🤗
Thanks!

tasmani osanseviero KingNish gospacedev victor NotASI MaziyarPanahi neel1796


Why Apache 2.0 Matters for LLMs 🤔

@01AI_Yi recently switched from a permissive & commercially friendly license, to Apache 2.0. And the community loved it! 🚀

@JustinLin610  also had a poll on model license and the majority votes for Apache 2.0.

Why it is a Big Deal? ⬇

📚 Legal Simplicity: Custom licenses need costly & time-consuming legal review. Apache 2.0 is well-known & easier for legal teams to handle.

👩💻 Developer-Friendly: Legal docs are a pain for devs! Apache 2.0 is well-known and tech-friendly, making it easier for non-native developers to understand the implications too.

🔗 Easier Integration: Apache 2.0 is compatible with many other licenses, simplifying tasks like model merging with models of different licensing requirements.

🚫 No Permission Needed: Custom licenses often require explicit permission and additional documentation work of filling forms, creating barriers. Apache 2.0 removes this hurdle, letting devs focus on innovation.

There are a lot interesting discussions from
@JustinLin610 's poll: https://x.com/JustinLin610/status/1793559737482764375 which inspired this thread.

Any other thoughts? Let me know ^^


DeepSeekV2 is a big deal. Not only because its significant improvements to both key components of Transformer: the Attention layer and FFN layer.

It has also completed disrupted the Chines LLM market and forcing the competitors to drop the price to 1% of the original price.

---

There are two key components in Transformer architecture: the self-attention layer, which captures relationships between tokens in context, and the Feed-Forward Network (FFN) layer, which stores knowledge.

DeepSeek V2 introduces optimizations to both:

Attention layer normally uses KV Cache to reduce repetitive compute, but it consumes significant GPU RAM, limiting concurrent requests. DeepSeek V2 introduces Multi-head Latent Attention (MLA), which stores only a small latent representation, resulting in substantial RAM savings.

DeepSeek V2 utilizes 162 experts instead of the usual 8 as in Mixtral. This approach segments experts into finer granularity for higher specialization and more accurate knowledge acquisition. Activating only a small subset of experts for each token, leads to efficient processing.

It disrupted the market by dropping API prices to $0.14 per 1M tokens. This dramatic reduction forced competitors like GLM, Ernie, and QWen to follow suit, lowering their prices to 1% of their original offerings. Now, users can access these APIs at 1/35th the cost of ChatGPT-4o.


**Model Names:** gpt-4-turbo-preview, gpt-4-vision-preview, gpt-3.5-turbo-16k
**Searchable Models:** Creative, Balanced, Precise

Image creation will be available soon in NiansuhAI.
**Model Name:** DALL-E 3

https://huggingface.co/spaces/NiansuhAI/LLMs1
---


Disappointed that Golden Gate Claude couldn't process images? Want to learn how to use activation vectors to steer VLMs?

Try out the https://huggingface.co/spaces/vikhyatk/contemplative-moondream space, and check out the notebook I released showing how to obtain control vectors! ⬇

https://github.com/vikhyat/moondream/blob/main/notebooks/RepEng.ipynb


YOLOv10: Real-Time End-to-End Object Detection (2405.14458)

🔥🚀🌟 New Research Alert - YOLOv10! 🌟🚀🔥
📄 Title: YOLOv10: Real-Time End-to-End Object Detection 🔝

📝 Description: YOLOv10 improves real-time object recognition by eliminating non-maximum suppression and optimizing the model architecture to achieve state-of-the-art performance with lower latency and computational overhead.

👥 Authors: Ao Wang et al.

📄 Paper: https://huggingface.co/papers/2405.14458

🤗 Demo: https://huggingface.co/spaces/kadirnar/Yolov10 curated by @kadirnar
🔥 Model 🤖: https://huggingface.co/kadirnar/Yolov10

📁 Repository: https://github.com/THU-MIG/yolov10

📮 Post about YOLOv9 - https://huggingface.co/posts/DmitryRyumin/519784698531054

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #YOLOv10 #ObjectDetection #RealTimeAI #ModelOptimization #MachineLearning #DeepLearning #ComputerVision #Innovation

DmitryRyumin kadirnar Taylor658 Etherll Saugatkafley Akhilsonga ajibawa-2023 louisbrulenaudet


When was the last time you looked for a non-English LLM, only to be saddened there is NO good option? 😟

For instance, @Meta 's poster child Llama 3 has less than 5% non-English tokens! 🌍

@cohere is here to change that... and change they will. 💪

Introducing Aya (this time the name actually makes sense) 🌟

Aya is an open-weight (CC-BY-NC) model, that comes in 3 flavours:

1⃣ Aya 101: 13B parameters, mT5-xxl based model that supports 101 languages 🌐

2⃣ Aya 23: 8B and  3⃣35B parameter models, supports 23 languages 📊

23 languages covered are: Arabic 🇸🇦, Chinese (simplified & traditional) 🇨🇳, Czech 🇨🇿, Dutch 🇳🇱, English 🇬🇧, French 🇫🇷, German 🇩🇪, Greek 🇬🇷, Hebrew 🇮🇱, Hindi 🇮🇳, Indonesian 🇮🇩, Italian 🇮🇹, Japanese 🇯🇵, Korean 🇰🇷, Persian 🇮🇷, Polish 🇵🇱, Portuguese 🇵🇹, Romanian 🇷🇴, Russian 🇷🇺, Spanish 🇪🇸, Turkish 🇹🇷, Ukrainian 🇺🇦, and Vietnamese 🇻🇳.

Not only models, they have open-sourced the dataset: Aya Collection stands as the most extensive assembly of multilingual instruction fine-tuning datasets to date, featuring 513 million prompts and completions across 114 languages. 📚

These annotations were provided by people across the globe. Not gonna lie, I almost shed a tear reading this... 😢

From Cohere: The word Aya is derived from the Twi language meaning “fern” - a symbol of endurance and resourcefulness. Aya embodies our dedication to advancing multilingual AI. 🌿

Looks like the good folks at Cohere did not sleep after the success of command r & command r plus! 😴➡🚀

Models: Aya-101-13B: https://huggingface.co/CohereForAI/aya-101
Aya-23-8B: https://huggingface.co/CohereForAI/aya-23-8B
Aya-23-35B: https://huggingface.co/CohereForAI/aya-23-35B

Dataset: https://huggingface.co/collections/CohereForAI/aya-datasets-660415741bd4852f01c81c77

Blog: https://cohere.com/research/aya


https://huggingface.co/nroggendorff/cats now has over 500 downloads?
what are you guys doing???


Researchers from Anthropic managed to extract millions of interpretable features from their Claude 3 Sonnet model, making it easier to identify and understand specific behaviors and patterns within the model.

This advance in understanding closed source AI models could make them safer by showing how specific features relate to concepts and affect the model’s behavior.

Read the Article: https://www.anthropic.com/research/mapping-mind-language-model?utm_source=substack&utm_medium=email

Read The Paper: https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html


𝐍𝐞𝐰 𝐠𝐮𝐢𝐝𝐞 𝐢𝐧 𝐨𝐮𝐫 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞 𝐀𝐈 𝐜𝐨𝐨𝐤𝐛𝐨𝐨𝐤: 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙙 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙤𝙣! ✨

Many use LLM use cases involve generating outputs with a specific structure.

➡ For instance when using an LLM as a judge to evaluate another model's outputs, you need it to give you not only a score, but also the rationale for this score, and maybe a confidence level.
So you do not need only "score: 1", but more a dictionary like:
```
{
     "rationale": "The answer does not match the true answer at all."
     "score": 1,
     "confidence_level": 0.85
}
```

🤔 How to force your LLM to generate such a structured output?

🏗 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗱𝗲𝗰𝗼𝗱𝗶𝗻𝗴 is a great technique to generate structured output: you can specify a grammar (=set of rules) that the output should follow, and 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗱𝗲𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗵𝗲𝗻 𝗳𝗼𝗿𝗰𝗲𝘀 𝘁𝗵𝗲 𝗱𝗲𝗰𝗼𝗱𝗲𝗿 𝘁𝗼 𝗼𝗻𝗹𝘆 𝗽𝗶𝗰𝗸 𝘁𝗼𝗸𝗲𝗻𝘀 𝘁𝗵𝗮𝘁 𝗿𝗲𝘀𝗽𝗲𝗰𝘁 𝘆𝗼𝘂𝗿 𝗴𝗿𝗮𝗺𝗺𝗮𝗿.

I've created a guide to show you how to use it, both via our Inference API and locally using 𝘰𝘶𝘵𝘭𝘪𝘯𝘦𝘴!

👉 Read it here: https://huggingface.co/learn/cookbook/structured_generation

Thank you @stevhliu for your great help in improving it!


Hey HuggingFace, love your open source attitude and particularly transformers.js for embedding models! Your current integration "use this model" gives you the transformers.js code, but there is no quick way to really test a model in one click.
SemanticFinder (https://huggingface.co/datasets/do-me/SemanticFinder) offers such an integration for all compatible feature-extraction models! All you need to do is add a URL parameter with the model ID to it, like so: https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5. You can also decide between quantized and normal mode with https://do-me.github.io/SemanticFinder/?model=Xenova/bge-small-en-v1.5&quantized=false. Maybe that would do for a HF integration?
I know it's a small open source project, but I really believe that it provides value for devs before deciding for one model or the other. Also, it's much easier than having to spin up a notebook, install dependencies etc.. It's private, so you could even do some real-world evaluation on personal data without having to worry about third-party services data policies.
Happy to hear the community's thoughts!


Gloomy AI Image! 👀

(Prompt: Please create me a gloomy forest showing the moon it is a very dark hollow place)


The 100 models milestone on the https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard is successfully reached within 10 days after the leaderboard's release 🥳

https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct is still the king of the leaderboard 👑 with a 3.46 points difference compared to its successor https://huggingface.co/CohereForAI/c4ai-command-r-plus who took the 2nd place 🥈 from his younger brother https://huggingface.co/CohereForAI/c4ai-command-r-v01 that lives today in the 5th floor just behind https://huggingface.co/Ashmal/MBZUAI-oryx -3rd place 🥉- (AFAIK an experimental model from MBZUAI) and https://huggingface.co/core42/jais-30b-chat-v3 -4th place- from Core42.

PS : I should consider a career in sports commentary 😂
Would you recommend me to BeIN Sports 😀 ?


🚀 Meet MergeUI - an All-in-one UI for Exploring Merged LLMs on Hugging Face 🤗!

Model merging is a cool new technique for creating powerful language models for cheap (no GPU required). But it raises questions like:
- Which models should we merge?
- What merge strategies work best?
- How do different base models affect performance?

With MergeUI, you can easily:
- Visualise the family tree and lineage of any merged model.
- Explore benchmark performance of family trees from the Open LLM Leaderboard.
- Analyse the different merge strategies used.
- Check license information for merged models and their ancestors.

All this helps you explore and understand merged models, uncover valuable insights, and make better decisions for your projects.

Ready to dive in? Check out these links:
- 🧬 Try MergeUI - https://naskio-mergeui.hf.space
- 👨💻 Source Code - https://github.com/naskio/mergeui

Love this project? boost it on GitHub and share it with your network.
#merge #mergekit #leaderboard
https://huggingface.co/spaces/naskio/mergeui


Reducing Transformer Key-Value Cache Size with Cross-Layer Attention (2405.12981)

New preprint out with colleagues from MIT and IBM Research

https://huggingface.co/papers/2405.12981

We introduce a simple mechanism of sharing keys and values across layers, reducing the memory needed for KV cache during inference!!

Jaward DmitryRyumin osanseviero alielfilali01 whitebill


the musicgen max4live device

gary4live

now has a popup UI so you can minimize ableton and have it keep trying/retrying from the input audio you recorded in the DAW

while coding lol


MobileNetV4 -- Universal Models for the Mobile Ecosystem (2404.10518)

Aiming to keep `timm` (https://github.com/huggingface/pytorch-image-models) the go-to library for efficient image encoders for your mobile and edge devices, I've started working on an implementation of the new MobileNet-V4 model. Take a look at a short article I wrote about the model: https://huggingface.co/blog/rwightman/mobilenetv4

Paper: https://huggingface.co/papers/2404.10518


mlx port of karpathy’s minbpe 🤕
Minimal (byte-level) Byte Pair Encoding tokenizer. Algorithmically follows along the GPT2 tokenizer.
Code: https://github.com/Jaykef/mlx-minbpe


Introducing Kraken-LoRA – a lightweight version of Kraken that uses LoRA-Adapters as Experts based on the base model.

@fernandofernandes , me, @Crystalcareai , @ehartford created the Kraken-LoRA!

🔍 What’s the big deal?

✅ Size Consistency: While Kraken’s size increases with more Experts, Kraken-LoRA remains as compact as the base model (e.g., 8b if you use Meta-Llama3-8b-Instruct).
✅ VRAM Efficiency: Kraken-LoRA is highly VRAM efficient, maintaining the power of all experts without the bloat.
✅ Dynamic Adaptation: LoRA adapters are applied dynamically at runtime, following the routing process.
✅ High Efficiency: Enjoy increased efficiency without compromising performance, as long as the LoRA adapters match the base model.

💡 Conclusion: Kraken-LoRA empowers businesses to experience enhanced flexibility and performance from our architecture, enabling further scalability without sacrificing performance.

Check out the model here: https://huggingface.co/VAGOsolutions/Kraken-LoRA
Explore the code here: https://github.com/cognitivecomputations/kraken/tree/main/Kraken-LoRA

Have fun with Kraken-LoRA! 🐙


Switching from French to German to Chinese in the same discussion 😅

Impressive to see Cohere for AI's new Aya model multilingual capabilities.

- C4AI Aya 23 is a research open weights release
- 8 and 35 billion parameter models
- 23 languages supported

You can try it out here: https://huggingface.co/spaces/CohereForAI/aya-23

namanvats KingNish osanseviero tomaarsen julien-c isr431 not-lain


Cohere's Aya 8B & 35B 🔥
> Multilingual (23 languages), beats Mistral 7B and Llama3 8B in preference—open weights.

 capabilities:

🌍 **Multilingual Mastery**: Supporting 23 languages, including Arabic!

🏆 **Top Performer**: Outperforms Mistral 7B and Llama3 8B in user preference.

🔍 **Open Weights**: Access open weights for your research and projects.

🔗 **License**: CC-BY-NC with adherence to C4AI's Acceptable Use Policy.

💼 **Developed by**: Cohere For AI and Cohere.

Check out Aya 23 on Hugging Face , link is in comments

#AI #MachineLearning #NLP #Multilingual #Arabic #TechInnovation #OpenSource #CohereAI #AyaModel

Salama1429 monsoon-nlp osanseviero do-me Ramikan-BR louisbrulenaudet radkisson


Chameleon: Mixed-Modal Early-Fusion Foundation Models (2405.09818)

Good folks at @Meta have introduced Chameleon 🦎 (who names these things? 🤷♂)

Chameleon is an AI model that can work with multiple types of data, like text and images, all at once. 🖼📝

Before you start searching, as of this post, the model/code have not been open-sourced nor is there any commitment to open-source... sorry! 🚫🔓

Still, here is the technical stuff:

👉 Challenges with Current Systems:

📉 Fragmentation: Current multimodal models are often specialized for either text or image tasks, lacking unified approaches.

📊 Scalability: Existing systems struggle with scaling to handle complex, mixed-modal tasks without significant performance degradation.

🔄 Alignment: Aligning textual and visual modalities remains a technical challenge, often requiring separate processing pipelines.

👉 Objective:

🎯 Unified Modeling: Develop a single model capable of handling various multimodal tasks (text generation, image generation, image captioning, visual question answering) seamlessly.

👉 How It's Done 📘

Early-Fusion Architecture 🧠: Utilizes an early-fusion token-based approach to integrate text and image data from the beginning.

Stable Training 💪: Implements a tailored alignment recipe and specific architectural parameterization to ensure stability in mixed-modal settings.

Broad Evaluation 📊: Assesses the model across various tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed-modal generation.

👉 Results: (Fun fact they mention Llava-1.5 in comparison but never really share the results)

🏆 Performance: Chameleon achieves state-of-the-art results in image captioning and outperforms models like Llama-2 in text-only tasks.

⚖ Competitiveness: It shows competitive performance with models such as Mixtral 8x7B and Gemini-Pro.

👩⚖ Human Judgments: Matches or exceeds the performance of larger models, including Gemini Pro and GPT-4V

Paper:  https://huggingface.co/papers/2405.09818

osanseviero not-lain gokaygokay rennokki yizhilll shoaibahmed ocan0


Retrieving Texts based on Abstract Descriptions (2305.12517)

How can we use open LLMs to create data for training sentence similarity models?

One of the most exciting use cases for LLMs is generating synthetic datasets that can be used to train non-LLM models. In the past, gathering enough data was one of the most significant barriers to training task-specific models. LLMs can potentially help in this area.

I've just written a new blog post on using https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct to generate synthetic similarity data based on the approach from https://huggingface.co/papers/2305.12517.

https://huggingface.co/blog/davanstrien/synthetic-similarity-datasets


Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with
  Anchor Gaussian Guided Texture Warping (2405.12069)

🚀🎭🌟 New Research Alert - Gaussian Head & Shoulders (Avatars Collection)! 🌟🎭🚀
📄 Title: Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping 🔝

📝 Description: Gaussian Head & Shoulders is a method for creating high-fidelity upper body avatars by integrating 3D morphable head models with a neural texture warping approach to overcome the limitations of Gaussian splatting.

👥 Authors: Tianhao Wu et al.

📄 Paper: https://huggingface.co/papers/2405.12069

🌐 Github Page: https://gaussian-head-shoulders.netlify.app

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🚀 Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #3DModeling #NeuralAvatars #GaussianSplatting #HighFidelityAvatars #3DReconstruction #AvatarRendering #TextureWarping #ComputerGraphics #DeepLearning #ComputerVision #Innovation

DmitryRyumin AlekseiPravdin Ramikan-BR namanvats

Taylor658 Ramikan-BR dashfunnydashdash Vlansu


We are hiring a "Developer Experience Engineer for Inference" at Hugging Face! If you want to make it easier for millions of people to use modern machine learning inference, apply! You can either work from one of our offices e.g. in Paris or New York, or work fully remotely. Details: https://apply.workable.com/huggingface/j/E732F4B8FC/

Taylor658 EduardoPacheco damerajee mattmdjaga mdouglas osanseviero not-lain DeepMount00 Lewdiculous victor nbroad


The Google Deep Mind Team just released a new technical report on Gemini 1.5 Pro and Gemini 1.5 Flash.

in addition to architecture, benchmark and evaluation details, the report also provides a few real world use cases for the models such as professional task optimization and translation of lesser-known languages.

You can check out the full report here: https://storage.googleapis.com/deepmind-media/gemini/gemini_v1_5_report.pdf?utm_source=substack&utm_medium=email

KvrParaskevi osanseviero abdeljalilELmajjodi Edgar404


Thanks to @OzzyGT for pushing the new Anyline preprocessor to https://github.com/huggingface/controlnet_aux. Now you can use the https://huggingface.co/TheMistoAI/MistoLine ControlNet with Diffusers completely.

Here's a demo for you: https://huggingface.co/spaces/radames/MistoLine-ControlNet-demo
Super resolution version: https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL

```python
from controlnet_aux import AnylineDetector

anyline = AnylineDetector.from_pretrained(
    "TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
).to("cuda")

source = Image.open("source.png")
result = anyline(source, detect_resolution=1280)
```


I use mergekit regularly, and often enough get acceptable results without performing fine-tuning afterward. My current thinking is that DARE-TIES should be avoided when merging dense models, as the process of thinning inherently punches holes in models.

I've had success using SLERP merges to graft Mistral v0.1 models with Mistral v0.2 models to obtain the context length benefits of the latter, and am looking forward to experimenting with Mistral v0.3, which recently dropped.

Mars2050 Meggido osanseviero Lewdiculous TravelingMan


Hi HF Community!🤗

If you are excited about AlphaFold3, but upset because it is not open-source, I might have a solution to cheer you up a little bit:

https://huggingface.co/spaces/as-cle-bert/proteinviz

This is a space that lets you predict the 3D structure of proteins from their amino-acidic sequences, with the protein folding model https://huggingface.co/facebook/esmfold_v1: using this space is the perfect quick-start to become a Protein Scientist! (or maybe not, who knows...🤔)

In the meantime, if you are curious about what's going on with AlphaFold3 and want something Biologist🔬/Computer Scientist💻-friendly, you can also check out the latest community blog post I wrote: https://huggingface.co/blog/as-cle-bert/what-is-going-on-with-alphafold3 🚀

Have fun and enjoy open-source science!🧬


Interesting, I've just seen the my first HF spam on one of my new model uploads: https://huggingface.co/shisa-ai/shisa-v1-llama3-70b - someone has an SEO spam page as a HF space attached to the model!?! Wild. Who do I report this to?


I've begun adding valuable blog posts on using/creating synthetic datasets to my curated list.

I am starting with a great post by @MoritzLaurer on utilizing an open LLM to generate data for training a specialized Roberta model.

Read the blog post: https://huggingface.co/blog/synthetic-data-save-costs
See the rest of the list:  https://github.com/davanstrien/awesome-synthetic-datasets

ZennyKenny Taylor658 MoritzLaurer osanseviero


We're thrilled to share the latest milestone in our journey toward bringing AISAK to the world: the introduction of AISAK-TVI, our first natively multimodal model.

As AISAK edges closer to a potential release for users, each advancement, like the development of AISAK-TVI, brings us one step closer to realizing our vision of a comprehensive AI solution. With AISAK-TVI, we're pushing the boundaries of AI capabilities, enabling the processing of both textual and visual inputs with textual output, all within the AISAK ecosystem.

While the prospect of public, everyday usage of AISAK remains on the horizon, we must acknowledge the reality of operating within constraints of limited resources. The journey to a widespread release demands careful planning, rigorous testing, and ongoing refinement, tasks that require time, dedication, and support.

We recognize that achieving our goals requires collaboration and contribution from a diverse community of enthusiasts, experts, and innovators. If you're passionate about AI and eager to be part of our journey, we invite you to lend your expertise, insights, or resources to help accelerate the progress of AISAK.

Whether you're a developer, researcher, investor, or simply someone with a keen interest in shaping the future of AI, your contributions can make a meaningful difference. Reach out to us at mandelakorilogan@gmail.com to explore how you can get involved and contribute to the evolution of AISAK.

Thank you for your continued support and enthusiasm. Together, we're laying the groundwork for a future where AI enriches and empowers lives in ways we've only begun to imagine.

Warm regards,

Mandela Logan - AISAK Team
https://huggingface.co/aisak-ai/aisak-tvi
https://huggingface.co/collections/aisak-ai/aisak-65ddeeb08d0978de6114702f


The kraken has awakened!
A Game-Changer in LLM Flexibility and Performance!

Over the past few weeks, VAGO solutions teamed up with Cognitive Computations and HyperSpace to develop a groundbreaking architecture that redefines flexibility in combining different LLM into one model.

@fernandofernandes , me,  @Crystalcareai , @ehartford  created the Kraken!

 What Can It Do? 🐙
✅ Versatile Architecture: Kraken allows the seamless combination of LLMs with varying sizes, quantizations, and model architectures. It currently supports quantizations in 4-bit, 8-bit, and AWQ, with more on the way. And it runs on Hugging Face Transformers 4.40+

✅ Kraken Router: Utilizing a custom sequence classification model with a context length of 32k tokens, The Kraken Router directs inputs to the most suitable Expert based on their characteristics.

✅ Adaptability: Enhanced input formatting supports the model’s adaptability to diverse conversational contexts.

✅ Extreme Versatility: Easily swap experts within Kraken for your specific use cases without retraining the entire model. For example, if you've built a Kraken for coding in Python you can upgrade your Python model without retraining the router or add a C# model by retraining the router.

✅ Open Source Pipeline: We’re sharing the entire pipeline, including router creation, training, architecture setup, and Kraken inference, on JupyterNotebooks: https://github.com/cognitivecomputations/kraken

Kraken marks the beginning of an exciting new journey in #OpenSource LLM. Why? Because it empowers the open source community in accelerating the catch-up process to proprietary LLMs like #GPT and #Claude 🤩

We proudly introduce the very first 2 Kraken models, that integrates top-tier LLM and Multilingual capabilities:
https://huggingface.co/cognitivecomputations/Kraken
https://huggingface.co/VAGOsolutions/Kraken-Multilingual
 Right now it's supported by Hugging Face transformers library. Would love to see the integration into VLM and TGWI!

pabloce agenkit Johnz86 Crystalcareai Locutusque osanseviero NotASI seyf1elislam


we recently shipped fine-grained access tokens on Hugging Face Hub, which lets you create tokens with super specific permissions

for instance, if you want to collaborate with an external organization you don't want to use your write token since they can access everything you can access. instead you can set token access to repositories under that org only like below

osanseviero nisten SixOpen alielfilali01 Ramikan-BR


Thanks to the incredible collaboration of 14 community annotators, @davanstrien of HF and @dvilasuero et. al of Argilla, DIBT (https://huggingface.co/DIBT) is pleased to make available a Russian-language dataset of 500 of the best curated LLM prompts translated to Russian and available for use: https://huggingface.co/datasets/DIBT/MPEP_RUSSIAN.

More to come from the MPEP initiative! Interested in annotating or leading a language team? https://github.com/huggingface/data-is-better-together/tree/main/prompt_translation

osanseviero dvilasuero davanstrien gabrielmbmb


Can someone suggest me a good open source vision model which performs good at OCR?


Excited to share a new project to make journalists’ lives easier when gathering information!

Collecting data like lists, URLs, etc., from websites is not always easy (and sometimes painful). Web scraping requires technical skills that only a handful of people in each newsroom have.

I recently stumbled upon @scrapegraphai, a scraper that does the heavy lifting with AI for the user with a simple prompt in natural language. I asked them if they could integrate the Hugging Face Hub to use open-source models and created a no-code, easy-to-use interface on Gradio.

You can then save time and focus on storytelling!

🔧 How It Works
1. Input Your Prompt and Source URL
2. Click ‘Scrape and Summarize’
3. Receive Summarized Results

👩💻 Get Involved!
This is just the first version of the tool, and it’s pretty basic. I’ve uploaded it to the Journalists on Hugging Face community so we can work together on it. Whether you’re a developer, a data scientist, or a journalist with ideas, you can contribute to this project.

You can also copy this app to your own account or organization to customize it to your needs.

👉 Test the scraper here: https://huggingface.co/spaces/JournalistsonHF/ai-scraper

🤝 Join the Journalists on 🤗 community: https://huggingface.co/JournalistsonHF

evijit osanseviero louisbrulenaudet Emmanuellepicaud British-Rat


We open source a new image inpainting model PowerPaint v2-1:
https://huggingface.co/JunhaoZhuang/PowerPaint-v2-1


My Favourite songs

https://youtu.be/jJvDnYdD8JQ
Katy perry roar, fireworks.

Paula Abdul - Straight Up (Lyrics), Paula Abdul Rush Rush (Lyricshttps://www.youtube.com/watch?v=c_43Qf_d-CA, https://www.youtube.com/watch?v=eqSdQ5gJC7A

BLACKPINK, Selena Gomez - Ice Cream (Lyrics) https://www.youtube.com/watch?v=pcrnh069iBI

Lil Nas X - Old Town Road (Lyrics link not included)

(SUNO.AI IS A SOFTWARE WHERE YOU CAN CREATE YOUR ON MUSIC WITH VOCAL SINGERS WRITE A PROMPT OR ADD YOUR OWN LYRICS)


Google LLM(Multimodal) Medical Foundation Model Summary

1.Med-PaLM: Large language models encode clinical knowledge, https://www.nature.com/articles/s41586-023-06291-2
2.Med-PaLM 2: Towards Expert-Level Medical Question Answering with Large Language Models, http://arxiv.org/abs/2305.09617
3.Med-PaLM M: Towards Generalist Biomedical AI, http://arxiv.org/abs/2307.14334
4.Med-Gemini: Capabilities of Gemini Models in Medicine, https://arxiv.org/abs/2404.18416v2; Advancing Multimodal Medical Capabilities of Gemini, https://arxiv.org/abs/2405.03162


Journey With Me Into The Mind of Large Language Models: Interesting Findings in AnthropicAI's Scaling Monosemanticity paper.

One of the many unknowns with LLMs is the why behind the responses they give - it's unclear why certain responses are chosen over others. Which shows how little we know of what's happening inside these models.

To have a deeper sense of this, they tried Sparse Dictionary Learning on a larger model (Claude 3 Sonnet) - wherein they match patterns of neuron activations (named Features) to human interpretable meanings.

Now Dictionary Learning is a traditional ml technique that identifies recurring patterns of neuron activations across various contexts. Meaning, any internal state of the model can be expressed as a combination of a few active features rather than numerous active neurons.

They scaled up a more effective measure of dictionary learning using a Sparse Autoencoder (SAE). The SAE has an encoder that maps inputs to sparse high-dimensional features via linear transformation &  ReLU, and a decoder that reconstructs inputs from those features.

Three variants (of sizes: ~1M, ~4M & ~34M features) of the SAE were trained and across SAEs, <300 active features/token, >65% variance were explained. With dead features:  ~2% for 1M, 35% for 4M, 65% for 34M SAE. Implying better training could reduce dead features.

Experiments were conducted with these SAEs where they were applied to residual stream activations (RSAs) at the model's middle layer (why? 1. RSAs are smaller than MLP layers = low compute cost, 2. helps tackle "cross-layer superposition" issues - when features are spread across multiple layers instead of being isolated in specific layers, causing interpretation difficulties). These experiments revealed that Scaling Laws can help guide  training of these SAEs.

My favorite of course is the Basic Code Features - where the model attributed meaning to different code syntax elements similar to syntax highlighting in text editors.

victor osanseviero Ramikan-BR louisbrulenaudet


AMMeBa: A Large-Scale Survey and Dataset of Media-Based Misinformation
  In-The-Wild (2405.11697)

80% of fact-checked misinformation claims involve media, with a rise in AI-generated content in 2023, according to a new study, “A Large-Scale Survey and Dataset of Media-Based Misinformation In-The-Wild.” Worth a read for journalists, especially fact-checkers.

TL;DR:
• 📊 135,838 fact checks analyzed
• 📸 80% of these claims involve media
• 🎥 Videos became more common starting in 2022, now more than 60% of fact-checked claims that include media
• 🤖 AI-generated content was rare until Spring of 2023, and then dramatically increased
• 🖼 Image manipulations don’t require complex operations. Most of the time it’s context manipulations

• Read the paper here: https://huggingface.co/papers/2405.11697
• Take a look at the dataset: https://huggingface.co/datasets/academic-datasets/AMMeBa

Thanks @davanstrien  for spotting it!

LeroyDyer osanseviero BoredApeYachtClub Cuiunbo victor


My version of OpenAI's Sora just came out, try out it here https://huggingface.co/spaces/Kvikontent/Openai-Sora

Made with https://huggingface.co/ali-vilab/text-to-video-ms-1.7b, so it isn't official sora

Poojith-Mapakshi KingNish Norod78 Kvikontent


Microsoft Just Launched 3 Powerful Models

1. Phi 3 Medium (4k and 128k): A 14b Instruct tuned models that outperformed big models like Command R+ (104b), GPT 3.5 Pro, Gemini Pro, and is highly competitive with top models such as Mixtral 8x22b, Llama3 70B, and GPT 4.
https://huggingface.co/microsoft/Phi-3-medium-4k-instruct
DEMO: https://huggingface.co/spaces/Walmart-the-bag/Phi-3-Medium

2. Phi 3 Mini Vision 128k: A 4.5 billion-parameter, instruction-tuned vision model that has outperformed models such as Llava3 and Claude 3, and is providing stiff competition to Gemini 1Pro Vision.
https://huggingface.co/microsoft/Phi-3-vision-128k-instruct

3. Phi3 Small (8k and 128k): Better than Llama3 8b, Mixtral 8x7b and GPT 3.5 turbo.
https://huggingface.co/microsoft/Phi-3-small-128k-instruct

DmitryRyumin CocoSun victor KingNish osanseviero RakshitAralimatti Donutanti GPT007

Walmart-the-bag kroonen victor osanseviero RakshitAralimatti


whaaaaaat!?

posts on hf LFG...

my name's kev

and i'm building things with musicgen right now with one simple idea:

removing the text from ai music. musicgen specifically, is more like a little musician itself than other models, and musicians shouldn't have to describe what they want from each other. they jam.

using your audio as input, gary comes in with the fills.

suddenly, you got yourself a lil jam buddy.

max4live device and chrome extension coming, but on hf you can already play with a version that has dope UX thanks to gradio, and is free thanks to zero-gpu-explorers.

https://huggingface.co/spaces/thepatch/zero-gpu-slot-machine

midi2musicgen so you can begin from a starting point of chords/melody

https://huggingface.co/spaces/thepatch/micro-slot-machine

this one begins from a place of drums.

the micro model was trained from scratch by @pharoAIsanders420

first post here, not sure if i'm doing this right...


Timm Leaderboard space here:
https://huggingface.co/spaces/MohamedRashad/timm-leaderboard

Thanks goes to @rwightman for building timm 🤗


Hey it was good meeting you yesterday @MaziyarPanahi 🔥

thanks @mishig for setting this up

Let's make the Hub as useful as possible for the community ❤

victor MaziyarPanahi KingNish lunarflu not-lain Rybens Tkap0176 sueszli theainerd seanmiranda andito


Phi-3-Medium just came out! So far it's decent (fails a few riddles 😔), try it for yourself and let me know how it is.

Original Model: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct
Test it out: https://huggingface.co/spaces/Walmart-the-bag/Phi-3-medium *running on ZERO gpu*


Is finding cool models/datasets and uploading them to the hub considered data hoarding ? I can't stop, send help.
https://huggingface.co/collections/1aurent/dinobloom-models-664ba2ab533c5d0c9c4b07e9
https://huggingface.co/collections/1aurent/tia-toolbox-models-654a3c7994d809b4e740105b
https://huggingface.co/collections/1aurent/kaikoai-models-66636c99d8e1e34bc6dcf795
https://huggingface.co/collections/1aurent/medmae-models-6565b4b766a4bce451a32ba7
https://huggingface.co/collections/1aurent/transpath-models-65639c6730a88a2f1d04321d
https://huggingface.co/collections/1aurent/lunit-models-65639b4149f816b7989185b4
https://huggingface.co/collections/1aurent/tcga-brca-ssl-models-65639d42f725fc09726909f1
https://huggingface.co/collections/1aurent/deblurringmim-models-6567446811b2bbd6c2a23071


Loving the new ChatGPT Mac app.

You can now turn a drawing into a working app in less than a minute!

oliguei Taylor658 BoredApeYachtClub DandinPower KingNish

Salama1429 Clausss fluxdielectric BoredApeYachtClub


Empereur : A Python script to automate downloading models from the Hugging Face model hub.

https://github.com/EmpereurPirate/Empereur


Tired of writing Pandas code? 😩

If you are using VS Code, now you can use Data Wrangler from @Microsoft ! 🚀

It will convert your Pandas DataFrame to a rich and interactive user interface to view and analyze your data 📊, show insightful column statistics and visualizations 📈, and automatically generate Pandas code as you clean and transform the data. 🧹🔄

Supports everything you can think of...

- Data view 👀
- Data cleaning 🧼
- Data filtering 🔍
- Data summary/statistics 📊
- Data transformation 🔄
- Data missing values treatment ❓
- Adding new fields ➕

Everything in a simple open-source extension 🌟

https://marketplace.visualstudio.com/items?itemName=ms-toolsai.datawrangler

PS: I love Pandas 🐼, never tired of it... still, this is cool! 😎


A new paper, "Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning," was just published. The approach improves VLMs' decision-making abilities in goal-directed tasks.

This is accomplished with Chain-of-thought (COT) reasoning, which seriously enhances performance. Removing COT reasoning, however, drops effectiveness, highlighting its crucial role.

Check out the paper here: https://arxiv.org/abs/2405.10292


Do you want to improve AI in your language? Here's how you can help.

I'm exploring different AI techniques for an upcoming project in journalism, and I wanted to test a cool idea by @davanstrien, Data is better together, which aims to foster a community of people to create DPO datasets in different languages.

This project gives the opportunity to explore various concepts:
- Direct Preference Optimization (DPO)
- Synthetic data
- Data annotation
- LLM as a judge

1⃣ Take the Aya dataset of human-annotated prompt-completion pairs across 71 languages and filter it to include only those in the language you’re interested in.

2⃣ Use distilabel from Argilla to generate a second response for each prompt and evaluate which response is best.

Basicaly, DPO datasets have a chosen and a rejected responses to a question, which helps align models on specific tasks.  To quote Daniel: "Currently, there are only a few DPO datasets available for a limited number of languages. By generating more DPO datasets for different languages, we can help to improve the quality of generative models in a wider range of languages."

3⃣  Send this dataset and evaluations to the easy-to-use interface to evaluate the evaluations.

This is where you can help. :) You can rate the LLM evaluation of the prompt-responses pairs. For my example, I built a dataset in French. And without wanting to start a debate about homeopathy, the second result is clearly better in the example below!  https://huggingface.co/spaces/fdaudens/demo-aya-dpo-french

The final dataset can be found here: https://huggingface.co/datasets/fdaudens/aya_french_dpo

To contribute to other languages and learn more about synthetic data, you can also produce datasets in the language of your choice! Read more about the project: https://github.com/huggingface/data-is-better-together/blob/main/dpo/README.md

osanseviero dvilasuero gabrielmbmb davanstrien KvrParaskevi Rohitkhatri75436 Lyte constantinSch


RoHM: Robust Human Motion Reconstruction via Diffusion (2401.08570)

🚀🤖🌟 New Research Alert - CVPR 2024! 🌟🤖🚀
📄 Title: RoHM: Robust Human Motion Reconstruction via Diffusion 🔝

📝 Description: RoHM is a diffusion-based approach for robust 3D human motion reconstruction from monocular RGB(-D) videos, effectively handling noise and occlusions to produce complete and coherent motions. This method outperforms current techniques in various tasks and is faster at test time.

👥 Authors: Siwei Zhang et al.

📅 Conference: CVPR, Jun 17-21, 2024 | Seattle WA, USA 🇺🇸

📄 Paper: https://huggingface.co/papers/2401.08570

🌐 GitHub Page: https://sanweiliti.github.io/ROHM/ROHM.html
📁 Repository: https://github.com/sanweiliti/RoHM

🚀 Added to the CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers

📚 More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🔍 Keywords: #RoHM #HumanMotionReconstruction #DiffusionModels #3DAnimation #CVPR2024 #DeepLearning #ComputerVision #Innovation

DmitryRyumin spockbuddhaiv KvrParaskevi SalimZ04 letheviet Alkohole


A useful tool for journalists: AutoQuizzer generates a quiz from a URL. You can play the quiz, or let the LLM play it!

https://huggingface.co/spaces/deepset/autoquizzer


PREM-1B-CHAT QUANTIZED INTO Q4
THEN SERVED IN WEBGPU DEMO

OG model https://huggingface.co/premai-io/prem-1B-chat
Q4 model https://huggingface.co/ucalyptus/prem-1B-chat-onnx-q4
WEBGPU demo  https://huggingface.co/spaces/ucalyptus/prem-1B-chat-webgpu


🚨 Instruct-tuning impacts models differently across families! Qwen2.5-72B-Instruct excels on IFEval but struggles with MATH-Hard, while Llama-3.1-70B-Instruct avoids MATH performance loss! Why? Can they follow the format in examples? 📊 Compare models: https://huggingface.co/spaces/open-llm-leaderboard/comparator


Just watched @thomwolf tear down the over-hyped AGI narrative in 30 seconds - and it's refreshingly grounded.

No wild speculation about superintelligence timelines or consciousness. Just practical insights from someone who really understands the technology.

This is the kind of level-headed perspective that helps us focus on what AI can actually do today (which is already transformative) rather than getting lost in AGI fantasy. Worth your time if you want to understand AI progress without the hype.

Watch the full interview at CogX here: https://www.youtube.com/watch?v=IjL_6Th6Ea0

Aurelien-Morgan not-lain mrdbourke ZeroWw Joseph717171 OmbelineM


Are you a Professional Python Developer? Here is why Logging is important for debugging, tracking and monitoring the code

Logging
Logging is very important part of any project you start. It help you to track the execution of a program, debug issues, monitor system performance and keep an audit trail of events.

Basic Logging Setup
The basic way to add logging to a Python code is by using the logging.basicConfig() function. This function set up basic configuration for logging messages to either console or to a file.

Here is how we can use basic console logging
```
#Call built in library
import logging

# lets call library and start logging
logging.basicConfig(level=logging.DEBUG) #you can add more format specifier

# It will show on the console since we did not added filename to save logs
logging.debug('Here we go for debug message')
logging.info('Here we go for info message')
logging.warning('Here we go for warning message')
logging.error('Here we go for error message')
logging.critical('Here we go for critical message')

#Note:
# If you want to add anything in the log then do like this way
records=100
logging.debug('There are total %s number of records.', records)

# same like string format
lost=20
logging.debug('There are total %s number of records from which %s are lost', records, lost)
```

Logging to a File
We can also save the log to a file instead of console. For this, we can add the filename parameter to logging.basicConfig().

```
import logging
# Saving the log to a file. The logs will be written to app.log
logging.basicConfig(filename='app.log', level=logging.DEBUG)

logging.debug('Here we go for debug message')
logging.info('Here we go for info message')
logging.warning('Here we go for warning message')
logging.error('Here we go for error message')
logging.critical('Here we go for critical message')
```
You can read more on my medium blog https://medium.com/@imranzaman-5202/are-you-a-professional-python-developer-8596e2b2edaa


As the rapid adoption of chat bots and QandA models continues, so do the concerns for their reliability and safety. In response to this, many state-of-the-art models are being tuned to act as Safety Guardrails to protect against malicious usage and avoid undesired, harmful output. I published a Hugging Face blog introducing a simple, proof-of-concept, RoBERTa-based LLM that my team and I finetuned to detect toxic prompt inputs into chat-style LLMs. The article explores some of the tradeoffs of fine-tuning larger decoder vs. smaller encoder models and asks the question if "simpler is better" in the arena of toxic prompt detection.

🔗 to blog:  https://huggingface.co/blog/daniel-de-leon/toxic-prompt-roberta
🔗 to model: https://huggingface.co/Intel/toxic-prompt-roberta
🔗 to OPEA microservice: https://github.com/opea-project/GenAIComps/tree/main/comps/guardrails/toxicity_detection

A huge thank you to my colleagues that helped contribute: @qgao007, @mitalipo, @ashahba and Fahim Mohammad

TuringsSolutions ImranzamanML qgao007 mitalipo PetarMiladinov lunarflu darkzbaron


Since 2022 I have been trying to understand how to support advancement of the two best python patterns for AI development which are:
1. Streamlit
2. Gradio

The reason I chose them in this order was the fact that the streamlit library had the timing drop on gradio by being available with near perfection about a year or two before training data tap of GPT.

Nowadays its important that if you want current code to be right on generation it requires understanding of consistency in code method names so no manual intervention is required with each try.

With GPT and Claude being my top two for best AI pair programming models, I gravitate towards streamlit since aside from common repeat errors on cache and experimental functions circa 2022 were not solidified.
 Its consistency therefore lacks human correction needs.  Old dataset error situations are minimal.

Now, I seek to make it consistent on gradio side.  Why?  Gradio lapped streamlit for blocks paradigm and API for free which are I feel are amazing features which change software engineering forever.

For a few months I thought BigCode would become the new best model due to its training corpus datasets, yet I never felt it got to market as the next best AI coder model.

I am curious on Gradio's future and how.  If the two main models (GPT and Claude) pick up the last few years, I could then code with AI without manual intervention.  As it stands today Gradio is better if you could get the best coding models to not repeatedly confuse old syntax as current syntax yet we do live in an imperfect world!

Is anyone using an AI pair programming model that rocks with Gradio's latest syntax?  I would like to code with a model that knows how to not miss the advancements and syntax changes that gradio has had in the past few years.  Trying grok2 as well.

My IDE coding love is HF.  Its hands down faster (100x) than other cloud paradigms.  Any tips on models best for gradio coding I can use?

--Aaron


Hi, I am looking for a nano/micro llama-compatible model so I can train to run it on my 16 GB Mac in CPU mode. Do you have any recommendations? Thanks


🔥🔥🔥Introducing Oryx-1.5!
A series of unified MLLMs with much stronger performance on all the image, video, and 3D benchmarks 😍
🛠Github: https://github.com/Oryx-mllm/Oryx
🚀Model: https://huggingface.co/collections/THUdyh/oryx-15-6718c60763845525c2bba71d
🎨Demo: https://huggingface.co/spaces/THUdyh/Oryx
👋Try the top-tier MLLM yourself!

👀Stay tuned for more explorations on MLLMs!

andito John6666 crimeraaa YaTharThShaRma999 oceansweep teowu Zuyan lunarflu AdinaY ucyang kdub307 djuna


Lotus 🪷 is a new foundation model on monocular depth estimation ✨
Compared to previous diffusion-based MDE models, Lotus is modified for dense prediction tasks
Authors also released a model for normal prediction 🤗
Find everything in this collection https://huggingface.co/collections/merve/lotus-6718fb957dc1c85a47ca1210

John6666 AtAndDev KvrParaskevi lunarflu not-lain

lunarflu AtAndDev AdinaY not-lain thomwolf


# PyTorch == 2.5.0 Breaks Transformers' SDPAttention!

When you encounter "RuntimeError: cuDNN Frontend error: [cudnn_frontend] Error: No execution plans support the graph."

We can use workaround like this:

```python
torch.backends.cuda.enable_cudnn_sdp(False)
```

but this slow downs the performance gain from PyTorch 2.5.

Although it is fixed(not "fixed" but default option is turn-off the cuDNN SDPA) at here -- https://github.com/pytorch/pytorch/pull/138587 , but not released yet. (you need to install directly from source)

Fastest way for now : pip install "torch<2.5"

Ref: https://github.com/huggingface/diffusers/issues/9704#issuecomment-2422585273


The Mystery Bot 🕵♂ saga I posted about from earlier this week has been solved...🤗

Cohere for AI has just announced its open source Aya Expanse multilingual model. The Initial release supports 23 languages with more on the way soon.🌌 🌍

You can also try Aya Expanse via SMS on your mobile phone using the global WhatsApp number or one of the initial set of country specific numbers listed below.⬇

🌍WhatsApp - +14313028498
Germany - (+49) 1771786365
USA – +18332746219
United Kingdom — (+44) 7418373332
Canada – (+1) 2044107115
Netherlands – (+31) 97006520757
Brazil — (+55) 11950110169
Portugal – (+351) 923249773
Italy – (+39) 3399950813
Poland - (+48) 459050281

louisbrulenaudet kassiepdj KvrParaskevi TipsUp Felladrin


For those with an interest in JA language models, this Llama 3 70B test ablation looks like it is the current strongest publicly released, commercially usable, open model available. A lot of caveats I know, but it also matches `gpt-3.5-turbo-0125`'s JA performance, which is worth noting, and is tuned *exclusively* with the old shisa-v1 dataset (so it's chart position will be very short lived).

https://huggingface.co/shisa-ai/shisa-v1-llama3-70b

https://huggingface.co/datasets/augmxnt/ultra-orca-boros-en-ja-v1


🤗 SDXL Flash

✨ Introducing the new fast model SDXL Flash (Mini), we learned that all fast XL models work fast, but the quality decreases, and we also made a fast model, but it is not as fast as LCM, Turbo, Lightning and Hyper, but the quality is higher. Below you will see the study with steps and cfg.

🚀 Features of mini model:
It weighs less, consumes less video memory and other resources, and the quality has not dropped much.

👑 Our faster than regular model is better in quality than the coolest modern models such as JuggernautXL X, FluentlyXL v4 and others.

SDXL Flash: https://huggingface.co/sd-community/sdxl-flash
SDXL Flash Mini: https://huggingface.co/sd-community/sdxl-flash-mini

Jaward clem KingNish ehristoforu elcrei multimodalart radames dreamdrop-art

ehristoforu KhalidKarami multimodalart radames Xirs dreamdrop-art


@FinancialSupport and I  just released a new version of the Italian LLMs leaderboard https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard
  using the super useful  https://huggingface.co/demo-leaderboard template from @clefourrier.
We’ve evaluated over 50 models (base, merged, fine-tuned, etc.) from:
- Major companies like Meta, Mistral, Google ...
- University groups such as  https://huggingface.co/sapienzanlp or https://huggingface.co/swap-uniba
- Italian Companies like  https://huggingface.co/MoxoffSpA , https://huggingface.co/FairMind  or https://huggingface.co/raicrits
- Various communities and individuals
All models were tested on #Italian benchmarks #mmlu #arc-c #hellaswag, which we contributed to the opensource lm-evaluation-harness library from https://huggingface.co/EleutherAI.
Plus, you can now submit your model for automatic evaluation, thanks to  to  https://huggingface.co/seeweb  sponsored computation.
Curious about the top Italian models? Check out the leaderboard and submit your model!

https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard


Just released a new version of https://huggingface.co/vikhyatk/moondream2 - now supporting higher resolution images (up to 756x756)!

TextVQA score (which measures the model's ability to read and reason about text in images) is up from 53.1 to 57.2 (+7.7%). Other visual question answering and counting benchmark results are up ~0.5%.

clem KeilahElla Shinku Tsardoz not-lain Sylvestre dillfrescott Rybens


BLACK HOLE SDXL Lightning:

Prompt: a photo of a baby dragon
Steps: 4


You are happy that @Meta has open-sourced Llama 3 😃...

So you jump on @HuggingFace Hub to download the new shiny Llama 3 model only to see a few quintillion Llama 3's! 🦙✨

Which one should you use? 🤔

Not all Llamas are created equal! 🦙⚖

An absolutely crazy comparison experiment by Wolfram Ravenwolf (@Wolfram) might answer your question! 🧪🧙♂

- Comprehensive assessment of Llama 3 Instruct 70B and 8B models. 📊
- Tested 20 versions across HF, GGUF, and EXL2 formats. 🔄
- Methodology: The process tested translation capabilities and cross-language understanding, using deterministic generation settings to minimize random factors. Used German data protection training exams to evaluate cross-language understanding. 🌐📝
- Best performance from EXL2 4.5bpw quant, scoring perfect in all tests. 🏆✅
- GGUF 8-bit to 4-bit quants also performed exceptionally. 🌟
- Llama 3 8B unquantized is best in its size class but not as good as 70B quants. 📏🔍
- 1-bit quantizations showed significant quality drops. ⚠⬇

Best models:
- https://huggingface.co/turboderp/Llama-3-70B-Instruct-exl2/tree/5.0bpw
- https://huggingface.co/casperhansen/llama-3-70b-instruct-awq

Blog: https://huggingface.co/blog/wolfram/llm-comparison-test-llama-3

GPT007 clem jensbosseparra not-lain Ramikan-BR


After spending some time practicing tokenization, I have come to realize that the difficulties we face in understanding each other are analogous to the challenges faced by LLMs in processing and interpreting tokens - as in untrained tokens lead to out of distribution qualms.

One could think of how we understand as a process that involves trained tokens (known/learned facts) grappling with prompts/tweets/lessons from someone else. This process is distinct for each person - with unique encoding, decoding, merging and splitting patterns.

This distinction might as well be categorized in gpt levels lol, which brings the question what level of tokenizer are you?  GPT-2, GPT-3, GPT-4 or GPT-4o tokenizer:)

Papers:
- Neural Machine Translation of Rare Words with Subword Units (https://arxiv.org/abs/1508.07909)
- Learning to Compress Prompts with Gist Tokens
(https://arxiv.org/abs/2304.08467)
- Language Models are Few-Shot Learners
(https://arxiv.org/abs/2005.14165)
- Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models
(https://arxiv.org/abs/2405.05417)
- Language Models are Unsupervised Multitask Learners

Code:
https://github.com/karpathy/minbpe
https://github.com/openai/tiktoken
https://github.com/openai/gpt-2
https://github.com/google/sentencepiece


StarCoder 15b Instruct v0.1 Space w/ Llama.cpp & Code Completion!

Hey all! I made a little StarCoder space, it's up for fun, have at it, pls make tons of pr's and requests, I love to improve my work for you all!

https://huggingface.co/spaces/MrOvkill/starcoder-15b-instruct


I hardly tested this text generation merged model before releasing it a few months ago. Now it's been receiving attention recently in the form of quants.
https://huggingface.co/grimjim/kunoichi-lemon-royale-v2-32K-7B

Judging from download numbers for GGUF quants, people appear to be using it, and at least one person has a merge formula that incorporated the model.
https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF
https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2
https://huggingface.co/mradermacher/kunoichi-lemon-royale-v2-32K-7B-GGUF


𝗥𝗲𝗮𝗱 𝗙𝘂𝗹𝗹 𝐀𝐫𝐭𝐢𝐜𝐥𝐞:

𝗥𝗲𝗮𝗱 𝗙𝘂𝗹𝗹 𝐀𝐫𝐭𝐢𝐜𝐥𝐞:

𝐒𝐮𝐦𝐦𝐚𝐫𝐲 𝐨𝐟 𝐀𝐫𝐭𝐢𝐜𝐥𝐞- 📝

𝐒𝐮𝐦𝐦𝐚𝐫𝐲 𝐨𝐟 𝐀𝐫𝐭𝐢𝐜𝐥𝐞- 📝

# 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐭𝐨 𝐂𝐫𝐞𝐚𝐭𝐞 𝐒𝐢𝐦𝐢𝐥𝐚𝐫 𝐀𝐈 🧠

# 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐭𝐨 𝐂𝐫𝐞𝐚𝐭𝐞 𝐒𝐢𝐦𝐢𝐥𝐚𝐫 𝐀𝐈 🧠

(𝙉𝙊𝙏𝙀: 𝙎𝙪𝙢𝙢𝙖𝙧𝙮 𝙞𝙨 𝘼𝙄 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙚𝙙) ✅

(𝙉𝙊𝙏𝙀: 𝙎𝙪𝙢𝙢𝙖𝙧𝙮 𝙞𝙨 𝘼𝙄 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙚𝙙) ✅

Decoding GPT-4'o': Its Mechanisms and Creating Similar AI.

𝗥𝗲𝗮𝗱 𝗙𝘂𝗹𝗹 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://huggingface.co/blog/KingNish/decoding-gpt-4o

𝐒𝐮𝐦𝐦𝐚𝐫𝐲 𝐨𝐟 𝐀𝐫𝐭𝐢𝐜𝐥𝐞- 📝
# 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐜𝐬 𝐨𝐟 𝐆𝐏𝐓-𝟒’𝐨’: GPT-4’o’ operates through three main components 🛠

𝟏. 𝐒𝐮𝐩𝐞𝐫𝐂𝐡𝐚𝐭: Integrates image generation, QnA (image, document and video) for diverse interactions.
𝟐. 𝐕𝐨𝐢𝐜𝐞 𝐂𝐡𝐚𝐭: Merges TTS and STT for real-time, human-like audio responses, focusing on human interaction.
𝟑. 𝐕𝐢𝐝𝐞𝐨 𝐂𝐡𝐚𝐭: Utilizes Zero Shot Image Classification to enhance user interaction with visual information.

# 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐭𝐨 𝐂𝐫𝐞𝐚𝐭𝐞 𝐒𝐢𝐦𝐢𝐥𝐚𝐫 𝐀𝐈 🧠

𝟏. 𝐌𝐮𝐥𝐭𝐢𝐌𝐨𝐝𝐚𝐥𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Combines multiple models for a powerful, multifunctional AI.
𝟐. 𝐃𝐮𝐜𝐭 𝐓𝐚𝐩𝐞 𝐌𝐞𝐭𝐡𝐨𝐝: Uses different models or APIs for specific tasks without additional training.

The article provides an in-depth exploration of GPT-4’o’, its functionalities, and methods to create similar AI models. It emphasizes the model’s language support and its innovative approach to human-AI interaction. 💡🌐

(𝙉𝙊𝙏𝙀: 𝙎𝙪𝙢𝙢𝙖𝙧𝙮 𝙞𝙨 𝘼𝙄 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙚𝙙) ✅

AIIAR SerialKicked dillfrescott ycjcl868


With slurm figured out and ablations humming along, I though I'd update and post my understanding of the legal status of training data in Japan. It is in general, much clearer in the US: https://huggingface.co/blog/leonardlin/ai-training-data-in-japan


Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? (2405.05904)

🎭 You picked an LLM for your work but then you find out it hallucinates! 🤖

🤔 Your first thought might be to fine-tune it on more training data.... but should you? 🛠

📜 This is what @Google is exploring in the paper "Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?" 🕵♂

📘 When LLMs undergo supervised fine-tuning with new factual knowledge not present in their initial training data, there is a risk they might "hallucinate" or produce factually incorrect information. 🚨

🔍 The paper investigates how fine-tuning LLMs with new facts influences their ability to leverage pre-existing knowledge and the extent to which they generate errors. 📊

⚙Technical Setup:

🔧 Approach: They introduce a system named SliCK (this stands for Sampling-based Categorization of Knowledge, don't even bother understanding how) to categorize knowledge into four levels (HighlyKnown, MaybeKnown, WeaklyKnown, and Unknown) based on how well the model's generated responses agree with known facts. 🗂

📝 Experimental Setup: The study uses a controlled setup focusing on closed-book QA, adjusting the proportion of fine-tuning examples that introduce new facts versus those that do not. 🧪

👉 Here is the gist of the findings:

🚸 LLMs struggle to integrate new factual knowledge during fine-tuning, and such examples are learned slower than those consistent with the model's pre-existing knowledge. 🐢

📈 As LLMs learn from examples containing new knowledge, their propensity to hallucinate increases. 👻

⏱ Early stopping during training can mitigate the risks of hallucinations by minimizing exposure to unlearned new facts. 🛑

🧠 Training LLMs mostly with known examples leads to better utilization of pre-existing knowledge, whereas examples introducing new knowledge increase the risk of generating incorrect information. 🏗

📄 Paper: https://huggingface.co/papers/2405.05904 📚

Ksgk-fy DynaTechSystems Rybens osanseviero lamhieu kimleang123 moock


Free Guide: How to Fine-Tune and Prompt Engineer LLMs

While some of the most forward-thinking companies in the world are already using LLMs, few organizations have the bandwidth, compute, or money to train foundational models in-house. It’s become much more common to either fine-tune or prompt engineer existing LLMs for unique business needs. In this guide, you’ll learn:

• How to choose between fine-tuning and prompting
• Popular fine-tuning strategies and their trade-offs
• Tasks where fine-tuning excels vs. ones where it doesn’t
• Tips and current best practices for prompt engineering
• And a whole lot more!

Link: https://wandb.ai/site/resources/whitepapers/llm-fine-tuning


Mm what a good time for a new merge!

This is a merge of 6 models that were finetuned on llama3 8b. This has done pretty decent on some coding tasks, for the parameter size. I have looked through models because a lot of people cannot run 33B models (deepseek) for coding.

Original Model: https://huggingface.co/Walmart-the-bag/Llama-3-LizardCoder-8B

GGUF: https://huggingface.co/Walmart-the-bag/Llama-3-LizardCoder-8B-GGUF

Ramikan-BR Joseph717171 DynaTechSystems Rybens osanseviero


New Updates OpenGPT 4o
1. Live Chat  (also known as video chat) (very powerful and fast, it can even identify famous places and persons)
2. Powerful Image Generation

Test and give feedback of New features:
https://huggingface.co/spaces/KingNish/OpenGPT-4o

Future Updates
1. PDF Chat
2.  Human like speech (Using Parler tts expresso)
3. Multilingual support for voice chat

Suggest more features that should be added. 🤗

Edit: Live Chat is now very powerful (than prev)

Gavinssss KingNish victor DynaTechSystems Rybens osanseviero GPT007 hsengiv yahma Lewdiculous ucyang

Rohitkhatri75436 victor SvCy DynaTechSystems osanseviero GPT007 KingNish hsengiv Lewdiculous sk003cs


How many times have you said Pandas is slow and still kept on using it? 🐼💨

Get ready to say Pandas can be fast but it's expensive 😂

🙌 Original Limitations:

💻 CPU-Bound Processing: Traditional pandas operations are CPU-bound (mostly single-threaded😰), leading to slower processing of large datasets.

🧠 Memory Constraints: Handling large datasets in memory-intensive operations can lead to inefficiencies and limitations.

𝌣 Achievements with @nvidia RAPIDS cuDF:

🚀 GPU Acceleration: RAPIDS cuDF leverages GPU computing. Users switch to GPU-accelerated operations without modifying existing pandas code.

🔄 Unified Workflows: Seamlessly integrates GPU and CPU operations, falling back to CPU when necessary.

📈 Optimized Performance: With extreme parallel operation opportunity of GPUs, this achieves up to 150x speedup in data processing, demonstrated through benchmarks like DuckDB.

😅New Limitations:

🎮 GPU Availability: Requires a GPU (not everything should need a GPU)

🔄 Library Compatibility: Currently in the initial stages, all the functionality cannot be ported

🐢 Data Transfer Overhead: Moving data between CPU and GPU can introduce latency if not managed efficiently. As some operations still run on the CPU.

🤔 User Adoption: We already had vectorization support in Pandas, people just didn't use it as it was difficult to implement. We already had DASK for parallelization. It's not that solutions didn't exist

Blog: https://developer.nvidia.com/blog/rapids-cudf-accelerates-pandas-nearly-150x-with-zero-code-changes/

For Jupyter Notebooks:

```python
%load_ext cudf.pandas
import pandas as pd
```

For python scripts:

```shell
python -m cudf.pandas script.py
```


🚀 Stockmark-100b

Stockmark Inc. has developed and released one of Japan's largest commercial-scale Language Models (LLM) with 100 billion parameters, named "Stockmark-LLM-100b". This model significantly reduces hallucinations and provides accurate responses to complex business-related queries. Developed from scratch with a focus on Japanese business data, the model aims to be reliable for high-stakes business environments. It's open-source and available for commercial use.

Key highlights:
- The model reduces hallucinations—incorrect confident responses that AI models sometimes generate.
- Stockmark-LLM-100b can answer basic business questions and specialized queries in industries like manufacturing.
- The model's performance surpasses GPT-4-turbo in accuracy for business-specific queries.
- Evaluation benchmarks (VicunaQA) show high performance.
- Fast inference speed, generating 100-character Japanese text in 1.86 seconds.

https://huggingface.co/stockmark/stockmark-100b
https://huggingface.co/stockmark/stockmark-100b-instruct-v0.1

Detailed press release (in Japanese): https://stockmark.co.jp/news/20240516


llm-jp-eval is currently one of the most widely used benchmarks for Japanese LLMs and is half of WandB's comprehensive Nejumi LLM Leaderboard scoring. I was seeing some weirdness in results I was getting and ended up in a bit of a rabbit hole. Here's my article on evaling llm-jp-eval: https://huggingface.co/blog/leonardlin/llm-jp-eval-eval

I've setup a fork of Lightblue's Shaberi testing framework which uses LLM-as-a-Judge style benchmarks as something probably more representative of real world LLM strength in Japanese. Here's how the new base model ablations are looking:


Introducing StyleTTS 2 detector, an audio classification model to detect StyleTTS 2 vs human-generated content!

Dual-licensed under MIT/Apache 2.0.

Model Weights: https://huggingface.co/mrfakename/styletts2-detector
Spaces: https://huggingface.co/spaces/mrfakename/styletts2-detector

leonardlin KvrParaskevi osanseviero quyet7779


If you're a researcher or developing your own model 👀 you might need to take a look at huggingface's ModelHubMixin classes.
They are used to seamlessly integrate your AI model with huggingface and to save/ load your model easily 🚀

1⃣ make sure you're using the appropriate library version
```
pip install -qU "huggingface_hub>=0.22"
```
2⃣  inherit from the appropriate class
```python
from huggingface_hub import PyTorchModelHubMixin
from torch import nn

class MyModel(nn.Module,PyTorchModelHubMixin):
  def __init__(self, a, b):
    super().__init__()
    self.layer = nn.Linear(a,b)
  def forward(self,inputs):
    return self.layer(inputs)

first_model = MyModel(3,1)
```
4⃣ push the model to the hub (or use save_pretrained method to save locally)
```python
first_model.push_to_hub("not-lain/test")
```
5⃣  Load and initialize the model from the hub using the original class
```python
pretrained_model = MyModel.from_pretrained("not-lain/test")
```

DmitryRyumin KvrParaskevi DynaTechSystems osanseviero isidentical cc92yy3344 KingNish Wauplin Ramikan-BR

KvrParaskevi osanseviero louisbrulenaudet alielfilali01 KingNish Wauplin Ramikan-BR


📚 Introducing the 101 Billion Arabic Words Dataset

🌐 Exciting Milestone in Arabic Language Technology! hashtag#NLP hashtag#ArabicLLM hashtag#LanguageModels

🚀 Why It Matters:
1. 🌟 Large Language Models (LLMs) have brought transformative changes, primarily in English. It's time for Arabic to shine!
2. 🎯 This project addresses the critical challenge of bias in Arabic LLMs due to reliance on translated datasets.

🔍 Approach:
1. 💪 Undertook a massive data mining initiative focusing exclusively on Arabic from Common Crawl WET files.
2. 🧹 Employed state-of-the-art cleaning and deduplication processes to maintain data quality and uniqueness.

📈 Impact:
1. 🏆 Created the largest Arabic dataset to date with 101 billion words.
2. 📝 Enables the development of Arabic LLMs that are linguistically and culturally accurate.
3. 🌍 Sets a global benchmark for future Arabic language research.

🔗 Paper: https://lnkd.in/dGAiaygn
🔗 Dataset: https://lnkd.in/dGTMe5QV

- 🔄 Share your thoughts and let's drive the future of Arabic NLP together!

hashtag#DataScience hashtag#MachineLearning hashtag#ArtificialIntelligence hashtag#Innovation hashtag#ArabicData

DynaTechSystems osanseviero Manel-Hik Wajdi1976


Chameleon

Mixed-Modal Early-Fusion Foundation Models

https://huggingface.co/papers/2405.09818

We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents.

akashicmarga Taylor658 YaTharThShaRma999 victor Thedude2024 zolicsaki basedanarki J-LAB mindrage DynaTechSystems osanseviero sepilqi diwank KingNish dillfrescott maus lambdavi IzzyR


I got asked about PaliGemma's document understanding capabilities, so I built a Space that has all the PaliGemma fine-tuned doc models 📄📊📖
https://huggingface.co/spaces/merve/paligemma-doc


🎉 Happy to announce about the collection called "Blackhole". It is a black hole of high quality data in many fields, multilingual to train LLMs with SFT and DPO methods.
📦 There are now over 30++ high-quality datasets available so you can start creating interesting models. It will be updated in the future, glad if it helps someone.

https://huggingface.co/collections/lamhieu/blackhole-66473b7feec034b4fb70818a

Rohitkhatri75436 osanseviero Ramikan-BR dillfrescott Rybens


Just passed the 25 models milestone on the https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard 🥳

And now https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct is the new hero of the leaderboard beating https://huggingface.co/CohereForAI/c4ai-command-r-v01 by 5.43 points 🔥

Almost another 80 models are still PENDING ! So this might change very fast in the upcoming days


Easily convert your script-based datasets to Parquet and explore them in the dataset viewer. 🌟

🛠 Use @huggingface Datasets CLI:
$ 𝚍𝚊𝚝𝚊𝚜𝚎𝚝𝚜-𝚌𝚕𝚒 𝚌𝚘𝚗𝚟𝚎𝚛𝚝_𝚝𝚘_𝚙𝚊𝚛𝚚𝚞𝚎𝚝 𝚄𝚂𝙴𝚁𝙽𝙰𝙼𝙴/𝙳𝙰𝚃𝙰𝚂𝙴𝚃_𝙽𝙰𝙼𝙴

Learn more: https://huggingface.co/docs/datasets/main/en/cli#convert-to-parquet
#Data #AI

lhoestq merterbak victor Thedude2024 yjernite Norod78


Why salesforce removedSFR-Iterative-DPO-LLaMA-3-8B-R ? Any ideas?


Introducing Sailor-14B Model and Sailor2 Project 🚢

We're thrilled to announce the release of the Sailor-14B models, including the Base and the Chat versions!

✅Built upon the Qwen1.5-14B model, the Base version follows a similar procedure as our Sailor-7B model.
✅The Chat version is optimized using DPO on our in-house human preference dataset, yielding a better experience than our previous Chat models.

🏠Home: https://sailorllm.github.io
🤗Model: https://huggingface.co/sail/Sailor-14B-Chat
💻Demo: https://huggingface.co/spaces/sail/Sailor-14B-Chat

We're also excited to introduce the Sailor2 project, ✨ an open collaboration opportunity for the entire community! ✨

🌐 The Sailor2 project aims to build a LLM with ~30B parameters, optimized for multiple South-East Asian languages, including Cebuano, Indonesian, Khmer, Lao, Minangkabau, Malay, Burmese, Sundanese, Javanese, Thai, and Vietnamese.

🎯The model will undergo continual pre-training from a base model proficient in both Chinese and English using nearly 800B SEA tokens, with an expected performance comparable to the most advanced business models for the above SEA languages.

🤝 Contribute your data, expertise, and ideas to shape the future of open-source LLMs for the SEA region.

🌍 Everyone passionate about the SEA region is welcome aboard! Join the party and get involved by scanning the QR code! 🔍

Let's sail together and enjoy the journey!⚓

osanseviero victor dreamerdeo AlexLaw830 nbroad suthanhcong


Stable Cascade Full Tutorial for Windows, Massed Compute, RunPod & Kaggle — Predecessor of SD3 — 1-Click Install Amazing Gradio APP

Stable Cascade is another amazing model for Stability AI

Weights are published

Stable Cascade Full Tutorial for Windows — Predecessor of SD3–1-Click Install Amazing Gradio APP : https://youtu.be/q0cYhalUUsc

Stable Cascade Full Tutorial for Cloud — Predecessor of SD3 — Massed Compute, RunPod & Kaggle : https://youtu.be/PKDeMdEObNo


🎉 A new LLM is launched! 🚀
After checking if it's open-source or not, 🤔
you rush to see the benchmarks... 🏃♂💨

Which benchmark does everyone check first? 🔍

MMLU (Massive Multitask Language Understanding)? 📚

Benchmarks like MMLU reaching saturation... most of the time the performance does not translate to real-world use cases! 🌐❗

Meet MMLU-Pro, released by TIGER-Lab on @huggingface ! 🐯🌍

🧪 12,217 questions across biology, business, chemistry, computer science, economics, engineering, health, history, law, mathematics, philosophy, physics, and psychology carefully validated by humans 🧑🔬

🔟 Goes to 10 options per question instead of 4, this increase in options will make the evaluation more realistic and reduce random guessing 🎯

📊 56% of questions come from MMLU, 34% from STEM websites, and the rest from TheoremQA and SciBench 📈

🤖 LLMs with weak chain-of-thought reasoning tend to perform lower, indicating it is more challenging and representative of real-world expectations 🧠💡

Any guess who tops it and who bombs it? 🤔📉📈

GPT-4o drops by 17% (from 0.887 to 0.7149) 📉
Llama-3-70B drops by 27% (from 0.820 to 0.5541) 📉

🔗 https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro


I've been doing some evals and tuning, and this chat template repo maintained by @chujiezheng is great: https://github.com/chujiezheng/chat_templates

Here's also a simple script for checking what the output looks like:
```
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("augmxnt/shisa-7b-v1")
messages = [
    {'role': 'user', 'content': 'This is the first user input.'},
    {'role': 'assistant', 'content': 'This is the first assistant response.'},
    {'role': 'user', 'content': 'This is the second user input.'},
]

print()
print('Chat Template:')
print(tokenizer.chat_template)
print()
print('---')
print()

print(tokenizer.apply_chat_template(messages, tokenize=False))
```


More context for your Pascal GPU or older!

Update: Now available in the official releases of KoboldCpp!
[releases] https://github.com/LostRuins/koboldcpp/releases/latest

These are great news for all the users with GTX 10XX, P40...

Flash Attention implementation for older NVIDIA GPUs without requiring Tensor Cores has come to llama.cpp in the last few days, and should be merged in the next version of KoboldCpp, you can already try it with another fork or by building it.

[Mentioned KCPP fork] https://github.com/Nexesenex/kobold.cpp/releases/latest

[PR] https://github.com/ggerganov/llama.cpp/pull/7188

You should expect less VRAM usage for the same context, allowing you to experience higher contexts with your current GPU.

There have also been reported final tokens/second speed improvements for inference, so that's also grand!

If you have tried it, I'd like to hear your experiences with --flashattention so far, especially for this implementation and for the large number of Pascal (GTX 10XX, P40...) cards.

Discussion linked bellow, with more links to relevant information:

https://huggingface.co/LWDCLS/LLM-Discussions/discussions/11

Cheers!

Lewdiculous gate369 odyss3y fluffyone Ramikan-BR Bjorno


Just bought PRO but I don't understands how to use any image generation models in spaces beacause I don't know how to decode or process images after generation. Help!


Access to computational resources is key for democratizing AI, in all domains.

We cooked up something we're proud of: Hugging Face is committing $10 million in free GPUs to help developers create new AI technologies.

“AI should not be held in the hands of the few. With this commitment to open-source developers, we’re excited to see what everyone will cook up next in the spirit of collaboration and transparency.” — @clem

Read the exclusive by Kylie Robison: https://www.theverge.com/2024/5/16/24156755/hugging-face-celement-delangue-free-shared-gpus-ai

clem zaursamedov1 osanseviero mexicanamerican sa8 MexIvanov KingNish not-lain


Thank you @Niansuh for starting a space and sharing your AI assistant application in Narra AI!

https://huggingface.co/spaces/narra-ai/ChatGPT


Baal, our Bayesian Active Learning library is working on a major version and we want to know more about you!

If you use Baal for Active Learning, Uncertainty Estimation or Bayesian Deep Learning, we would **love** to talk to you! 😎

In more detail, we want to understand when our users use our library and how.

You can take a spot in our Calendly: https://calendly.com/baal-org/30min?month=2024-05


GPT-4o is "Clearly Programmed to Feed Dudes' Egos" - Desi Lydic on the Daily Show. https://www.youtube.com/watch?v=eFkUOi_9140&t=301s


I've just published the first part of a series on open problems in decentralized AI infrastructure .

This one focuses on verifiable computations, would love some feedback from the HF community!

https://link.medium.com/5YYUnNpcEJb


The Document AI team (@Molbap, @rwightman, @danaaubakirova)  at Hugging Face is  developing a new multimodal data augmentation pipeline utilising both visual and textual aspects of document images.

Check out my latest blog post for more details:
https://huggingface.co/blog/danaaubakirova/doc-augmentation

Please, share your thoughts and suggestions with us.
And stay tuned for the updates!

rwightman Molbap osanseviero not-lain de-Rodrigo


𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬

𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬

Do you want to play a game against Llama 3? 🦙🦙🦙

Meet 🧑🏫 𝐀𝐮𝐭𝐨𝐐𝐮𝐢𝐳𝐳𝐞𝐫, a new LLM application that you can use for learning or just for fun.

Try it out on Hugging Face Spaces 🤗 https://huggingface.co/spaces/deepset/autoquizzer

𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬
You provide an URL -> A multiple-choice quiz is instantly generated.

🔹 You can play the quiz yourself.

🔹 You can let the LLM play in two different ways
📕 Closed book: the LLM responds only by knowing the general topic and using its parametric knowledge and reasoning abilities.
🔎🌐 Web RAG: for each question, a Google search is done and the top 3 snippets are included in the prompt for the LLM.

𝐒𝐭𝐚𝐜𝐤
🏗 Haystack LLM framework https://haystack.deepset.ai/
🦙 Llama 3 8B Instruct
⚡ Groq

Original idea: @Tuana

Tuana KvrParaskevi osanseviero afrideva DavidGF Ramikan-BR


Ilaria RVC Mainline was updated to provide lightning fast text or audio to audio cloning via Huggingface!

It also includes super useful features for all users.

Don't forget to heart the spaces!

New Version: https://huggingface.co/spaces/TheStinger/Ilaria_RVC_Mainline

Old Version: https://huggingface.co/spaces/TheStinger/Ilaria_RVC


At Google I/O 2024, we're collaborating with the Google Visual Blocks team (https://visualblocks.withgoogle.com) to release custom Hugging Face nodes. Visual Blocks for ML is a browser-based tool that allows users to create machine learning pipelines using a visual interface. We're launching nodes with Transformers.js, running models on the browser, as well as server-side nodes running Transformers pipeline tasks and LLMs using our hosted inference. With @Xenova @JasonMayes

You can learn  more about it here https://huggingface.co/blog/radames/hugging-face-google-visual-blocks

Source-code for the custom nodes:
https://github.com/huggingface/visual-blocks-custom-components

osanseviero Eyel awacke1 lisaterumi Taylor658 3thn takeraparterer FaizanMunsaf Maaan KingNish Iwaku-Real


text-generation-inference v2.0.3 is out.

Main new features:
- Falcon2 support
- PaliGemma support
- New faster speculation method from IBM !

https://github.com/huggingface/text-generation-inference/releases

radames osanseviero Molbap KingNish Taylor658


So many of you have asked how to do segmentation and detection with PaliGemma, you've been served! 🫡
Here's the notebook to do so: https://colab.research.google.com/drive/16-Tq-iAMHNlSjDWgz43kYDMJERjU_KHW?usp=sharing 🤗


Hello world! My first post on Hugging Face 🤗 :)


`timm` 1.0 is finally out. The big feature that I wanted to complete before doing this? Having the unified feature map extraciton interface (features_only=True) supporting almost all models (97%) 🎉 See docs at https://huggingface.co/docs/timm/en/feature_extraction

Also in this release, the new set of SBB (searching for better baselins) ViT models, covering new architectures and hparam exploration between tiny and base. See https://huggingface.co/collections/timm/searching-for-better-vit-baselines-663eb74f64f847d2f35a9c19

I also snuck in image-tower loading for PaliGemma (via jax weights on Hub) https://huggingface.co/collections/google/paligemma-release-6643a9ffbf57de2ae0448dda

taufiqdp osanseviero mattmdjaga uisikdag evdcush zpn liuyuex


Another excellent course has launched on Hugging Face Learn https://huggingface.co/learn

HF Developer Advocate Dylan Ebert has officially launched the ML for 3D Course! 🤗

Check it out @ https://huggingface.co/learn/ml-for-3d-course/unit0/introduction

YT Channel: https://www.youtube.com/@IndividualKex
GitHub: https://github.com/huggingface/ml-for-3d-course


it's raining vision language models ☔
CuMo is a new vision language model that has MoE in every step of the VLM (image encoder, MLP and text decoder) and uses Mistral-7B for the decoder part 🤓
You can try it yourself here: https://huggingface.co/spaces/shi-labs/CuMo-7b-zero

the authors firstly did pre-training of MLP with the by freezing the image encoder and text decoder, then they warmup the whole network by unfreezing and finetuning which they state to stabilize the visual instruction tuning when bringing in the experts. 🤓

the mixture of experts MLP blocks above are simply the same MLP blocks initialized from the single MLP that was trained during pre-training and fine-tuned in pre-finetuning.
it works very well (also tested myself) that it outperforms the previous sota of it's size LLaVA NeXt and IDEFICS2-8B in several benchmarks! 😍

KvrParaskevi takeraparterer alielfilali01 iKyalo Saugatkafley


Something is wrong with GPT-4o

Today, I gained access to GPT-4o, so I thought to test it. However, I encountered several problems, such as When I requested image generation, it did not create any images but only provided links, which are also incorrect. 😥 [Image 1]

Subsequently, I considered that my prompt might be incorrect, I attempted once more with a prompt from OpenAI's examples, but it also did not work. 😥 [Image 2]

Then, I tested its logical reasoning skills, which it failed. I presented a question that an 8b model solved with ease, but GPT-4o could not. 😥 [Image 3]

I also attempted to generate an image from another image, but this too was unsuccessful. [image 4]

Nonetheless, it excels in tasks such as image classification and voice chat.

If you've experienced similar issues, please share them here.


ChatGPT 3.5 + 4 + BingAI

Use Now: https://niansuhai-llms.hf.space

https://huggingface.co/spaces/NiansuhAI/LLMs

Niansuh Taylor658 Ramikan-BR Makya Akemidia


📣 Happy to introduce, CinePile, a long video QA dataset and benchmark! 300k train and 5k test split. The dataset is live on HF right now! :)

Link: https://huggingface.co/datasets/tomg-group-umd/cinepile

Pls check it out and let us know if you have any comments or suggestions! :)


Which one is your favourite? Tell me in the comments

*Created using suno.ai and Microsoft co-pilot. all rights and reserves are mine, if you want to use it ask for my permission first*


After saying AI only 120 times, we have the next family of Gemma models!

🆓Gemma Model Family:
🦙 Gemma 2: 27B parameter model, comparable to Llama-3-70B.

👁 PaliGemma: PaliGemma is a new vision-language model family from Google, combining SigLIP as an image encoder and Gemma-2B as a text decoder. 🌟🖼📝

📽Moren technical Details of PaliGemma:

👉Model Types: Pretrained (PT) 🏋♂, Mix (general-purpose) 🔄, and Fine-tuned (FT) 🎯.
👉Resolutions and Precisions: 224x224, 448x448, 896x896; bfloat16, float16, float32. 📏📐
👉Tasks: Image captioning 🏞🖊, visual question answering (VQA) ❓🤔, detection 🔍, referring expression segmentation ✂, document understanding 📄.
👉Architecture: SigLIP-So400m for images 🖼, Gemma-2B for text 📝.
👉Inference: Via PaliGemmaForConditionalGeneration class in transformers 🤖.
👉Fine-tuning: Available using both big_vision and transformers frameworks 🔧🛠.
👉Memory Considerations: Higher resolution models require more memory 🧠💾.

✨ Other things ✨ (Why is everyone using ✨ for AI? 🤨)

🆒 Gemini Model Family:

🌟 Gemini 1.5 Pro: 2m token support, quality improvements in translation, coding, reasoning.
⚡ Gemini Flash: Optimized for speed, 1m token capacity.
🚀 Gemini Ultra, Pro, Flash, Nano: Various performance and efficiency models.
💎 Gemini Gems: Custom GPTs.
🎙 Gemini Live (Project Astra): Two-way voice conversation.
📚 LearnLM: Models fine-tuned for learning.

🆕Other Launches:

🤖 Veo: DeepMind's answer to Sora.
🖼 Imagen 3: Improved photorealistic image generation.
🎵 Music AI Sandbox: YouTube x DeepMind collaboration.
🔏 SynthID watermarking: Extends to text, images, audio, video.
⚙ Trillium (TPUv6): New TPU.

💲@Google Product Suite Integrations: Enhancements in Workspace, Email, Docs, Sheets, Photos, Search, Android, and Lens.

https://huggingface.co/collections/google/gemma-release-65d5efbccdbb8c4202ec078b

https://huggingface.co/collections/google/paligemma-release-6643a9ffbf57de2ae0448dda


The Golden king Ai #openjourney #nijijourney


https://huggingface.co/unsloth just crossed 1M+ downloads! 🤯

Some of the most popular 👀 :
https://huggingface.co/unsloth/llama-3-8b-bnb-4bit
https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit
https://huggingface.co/unsloth/mistral-7b-instruct-v0.2-bnb-4bit

not-lain taufiqdp heysourin victor osanseviero Ramikan-BR EduardoPacheco

not-lain monsoon-nlp osanseviero Ramikan-BR


Yesterday was just CRAZY ! HF x LangChain, PaliGemma and Google I/O ... which made me totally forget posting here about our newly released leaderboard (The Open Arabic LLM Leaderboard - OALL)

Here's a quick update for our community that is waiting for new results. Some of you noticed that since the release yesterday, the finished evaluations tab has stayed at 14 models up until now (May 15th, 12 PM). For those concerned, rest assured—we had a minor memory issue in our cluster yesterday that we overlooked. The problem is now fixed, and 7 models are currently being evaluated in parallel, so expect to hit the 20 milestone today! 🎉

Check the discussion below for more info :

https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard/discussions/3

lunarflu not-lain medmac01 aari1995 victor osanseviero imomayiz


After the announcements yesterday, I got a chance to try the new gemini-1.5-flash model from @goog1e, it is almost as good as gpt-4o on the StaticAnalaysisEval (https://huggingface.co/datasets/patched-codes/static-analysis-eval) It is also a bit faster than gpt-4o and much cheaper.

I did run into a recitation flag with an example in the dataset where the api refused to fix the vulnerability and flagged the input as using copyrighted content. This is something you cannot unset even with the safety filters and seems to be an existing bug https://issuetracker.google.com/issues/331677495

But overall you get gpt-4o level performance for 7% the price, we are thinking of making it default in patchwork - https://github.com/patched-codes/patchwork You can use the `google_api_key` and `model` options to choose `gemini-1.5-flash-latest` to run it with patchwork.

codelion lunarflu victor alias8797 osanseviero


OPEN VLM LEADERBOARD JUST RELEASED the FULL EVALUATION RESULTS of GPT-4o

[TL;DR]
GPT-4o shows steady progress compared to GPT-4v (0419), with a 3% improvement on the average score (68.7% -> 72.1%). GPT-4o displays stronger perception and less hallucination.

https://huggingface.co/spaces/opencompass/open_vlm_leaderboard

osanseviero Norod78 lunarflu KennyUTC victor danielus nebulae09 dillfrescott


💡 Thinking Tokens For Language Models!

How much is 56 times 37? Can you answer that right away?

In a short paper, David Herel and Tomas Mikolov propose a simple method to improve the reasoning of language models when performing complex calculations.

📌 They note that, although language models are not that good with difficult calculations, humans also cannot perform these calculations immediately and require a considerable amount of time to come up with an answer.

Inspired by this, they introduce 💡Thinking Tokens💡

So what are those "thinking tokens"?! Nothing fancy, they are just special tokens '<T>' that you insert after each word in a sentence whenever a complex problem is encountered. That's it!

👉 The main idea is to "buy" the model "some time" to think about the problem with these additional computations before answering. Using this method they observed an improved (a little bit) perplexity.

👉 Before getting excited note that: They have added these tokens manually, and they have used an RNN language model. From the paper:

"As a proof of concept, we have added N ’thinking tokens’ (< T >) after each observed word in a dataset. Our vision is that this basic concept can be extended to a self-adjusting model, which will be able to decide itself if and how many ’thinking tokens’ will be used for a specific problem, where N could also vary throughout the sentence. This would allow us to reduce the computational time, which would not increase N times."


It turned out that the following simple method seems to be actually effective when you want to increase the appearance probability of only one or a very limited number of tokens.

```
import os

one_token = "♡" # Token to increase the appearance probability
value = 1000000

token = one_token * value

with open("one-token.txt", "w", encoding="utf-8") as f:
    f.write(token)
```

By training LoRA with unsloth based on the .txt file generated by the code above, you can increase the appearance probability of specific tokens while maintaining the model's performance to great extent. However, it's better to stop the training before train loss becomes 0.0, as it will start spamming the token once it appears even once. In general, you can stop training at a very early stage and it will still work.

It is also possible to reduce the appearance probability of specific tokens by creating an over-learned LoRA with the specific tokens you want to reduce, combining it with the model, and then creating a model that extracts only the difference using the chat vector method and subtracting it from an arbitrary model.

In this case, it is better to set the ratio of chat vector to about five times. It has very little effect on the overall performance, apart from the specific tokens.

```
        new_v = v - (5.0 * chat_vector[i].to(v.device))
```


I've been researching what makes us "conscious" and the ambiguity in the word "conscious", It all falls to knowledge and ability to use it.

Do you think we can upload consciousness using AI?


Is there a correlation between number of words and knowledge?
Sanskrit has 1000X more words than any other language.


I just completed getting all four aspects of the new OpenAI GPT-4-o Omni model to process Text, Image, Audio, and Video.

Check it out and let me know what you think!

Space:  https://huggingface.co/spaces/awacke1/GPT-4o-omni-text-audio-image-video

Discussion:  https://huggingface.co/spaces/awacke1/GPT-4o-omni-text-audio-image-video/discussions

Test Runs for All Four Modalities:  https://huggingface.co/spaces/awacke1/GPT-4o-omni-text-audio-image-video/discussions/1

--Aaron -  https://huggingface.co/awacke1

athareja osanseviero lunarflu taher30 zhenweiding Rajib-Mondal YokoH


New open Vision Language Model by @Google: PaliGemma 💙🤍

📝 Comes in 3B, pretrained, mix and fine-tuned models in 224, 448 and 896 resolution
🧩 Combination of Gemma 2B LLM and SigLIP image encoder
🤗 Supported in transformers

PaliGemma can do..
🧩 Image segmentation and detection! 🤯
📑 Detailed document understanding and reasoning
🙋 Visual question answering, captioning and any other VLM task!

Read our blog 🔖 hf.co/blog/paligemma
Try the demo 🪀 hf.co/spaces/google/paligemma
Check out the Spaces and the models all in the collection 📚  https://huggingface.co/collections/google/paligemma-release-6643a9ffbf57de2ae0448dda
Collection of fine-tuned PaliGemma models https://huggingface.co/collections/google/paligemma-ft-models-6643b03efb769dad650d2dda

lunarflu DmitryRyumin osanseviero KhalilGuetari clem aliorhun taufiqdp Molbap jaigouk damerajee not-lain letheviet Matinmollapur01

lunarflu osanseviero clem jucamohedano Molbap damerajee alvarobartt letheviet

lunarflu osanseviero clem nsankar07 damerajee not-lain


AI-town now runs on Hugging Face Spaces with our API for LLMs and embeddings, including the open-source Convex backend, all in one container. Easy to duplicate and config on your own

Demo: https://huggingface.co/spaces/radames/ai-town
Instructions: https://github.com/radames/ai-town-huggingface

lunarflu osanseviero reach-vb clem prabhatkr adamelliotfields linoyts julien-c asus4 pcuenq evdcush victor not-lain Jofthomas ldanilek

reach-vb clem awacke1 adamelliotfields julien-c evdcush not-lain

reach-vb clem julien-c evdcush not-lain jaisanrobert


Hello everyone,

I wanted to share some exciting news: Google has just launched PaliGemma, a new Gemma Model which is multimodal and has 3 billion parameters.

What do you all think about this development? Are you as intrigued by its potential as I am?

FlipTip lunarflu osanseviero clem hideosnes victor manvinder jyoung105