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] | Simplified implementation of “Neural Networks are Decision Trees”.
Showing that any neural network with any activation function can be represented as a decision tree. Since decision trees are inherently interpretable, their equivalence helps us understand how the network makes decisions.
In this implementation, we trained a simple neural network for 1k epochs on makemoons, saved the trained weights (state dicts), extracted the decision tree equivalent from the trained weight then visualize and evaluate.
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Excuse me.
I would like to report the following bug or new specification that is probably the cause of the fatal stacks that are occurring in the Zero GPU space throughout HF.
Thanks.
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] | NVIDIA just dropped NVEagle 🦅
Super impressive vision language model that comes in 7B, 13B and 13B fine-tuned on chat 💬
Model repositories: https://huggingface.co/collections/merve/nveagle-66d0705108582d73bb235c26
Try it: https://huggingface.co/spaces/NVEagle/Eagle-X5-13B-Chat 💬 (works very well! 🤯)
This model essentially explores having different experts (MoE) for image encoder part of vision language model.
How? 🧐
The authors concatenate the vision encoder output tokens together, and they apply "pre-alignment" essentially fine-tune experts with frozen text encoder.
Then they freeze both experts and the decoder and just train the projection layer, and finally, they unfreeze everything for supervised fine-tuning ✨
In the paper, they explore different fusion strategies and vision encoders, extending basic CLIP encoder, and figure out simply concatenating visual tokens works well.
Rest of the architecture is quite similar to LLaVA. (see below the architecture) | {
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] | 📫 A packed AI in the News edition today!
📉 Nvidia Revenue Jumps 122% in Positive Sign for Tech's A.I. Boom - NYT
- $30.04 billion revenue, $16.95 billion net income (up from $6.19 billion a year ago)
- Shares in the company fell by as much as 7% in after-hours trading
- Nvidia faces production challenges with its new Blackwell chip and growing competition, including from its own customers
- Spending on data centers and energy costs to support A.I. is expected to be $1 trillion
👉 https://www.nytimes.com/2024/08/28/technology/nvidia-earnings-ai-stocks.html
🏛️ California Legislature Approves Bill Proposing Sweeping A.I. Restrictions - NYT
- Bill S.B. 1047 would require AI companies to test their systems for safety before public release and allow the state attorney general to sue for serious harms caused by AI.
- Supporters argue it’s necessary to mitigate AI risks, while critics worry it’s excessively focused on catastrophic harms and could jeopardize open-source AI development.
- Governor Gavin Newsom has until September 30 to decide on the bill, which could set a national standard for AI regulation if signed into law.
👉 https://www.nytimes.com/2024/08/28/technology/california-ai-safety-bill.html
🧑🏫 Generative AI Transformed English Homework. Math Is Next
- Gauth app, which can solve math problems from photos, has millions of downloads
- Got a low B in high-school level algebra and geometry in tests by Wired. "Likely good enough to satisfy bored students who'd rather spend their time after school doing literally anything else."
- The rise of such AI tools challenges educators to rethink their approach to math homework and teaching methods, possibly leading to a shift towards more in-class practice and personalized learning.
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] | 💬 🇮🇹 Phi 3.5 mini ITA: a Small Language Model for Italian
Lately, I've spent some time fine-tuning language models.
Now I am happy to release Phi 3.5 mini ITA: a fine-tuned version of Phi-3.5-mini-instruct to improve performance on the Italian language
🔹 Small (3.82 B parameters) but capable model
🔹 128k context length
Chat with it on 🤗 Spaces: https://huggingface.co/spaces/anakin87/Phi-3.5-mini-ITA
Model card: https://huggingface.co/anakin87/Phi-3.5-mini-ITA
🗃️ Data
Supervised fine-tuning using a good mix of English and Italian data:
- https://huggingface.co/datasets/mlabonne/FineTome-100k by @mlabonne
- https://huggingface.co/datasets/efederici/capybara-claude-15k-ita by @efederici
🙏 Thanks to the authors for the datasets.
🎯 Targeted training with Spectrum
I used Spectrum, a relatively new technique for parameter-efficient learning.
The idea is to train only the layers of the model with high Signal-to-Noise Ratio (SNR) and ❄️ freeze the rest.
I trained the top 30% of model layers.
📝 Spectrum paper: https://arxiv.org/abs/2406.06623
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] | 1. **Overview**
"EveryText" is at the forefront of AI image generation, offering a novel "TBF ('Text by Font') Image Model" that enables the representation of all languages globally in AI-generated images without prior training.
2. **Background**
Platforms like MidJourneyV6 and FLUX have advanced AI image generation, typically supporting English text. Alibaba Group expanded this to include Chinese, Japanese, and Korean, signaling a shift towards global language support.
3. **Challenges**
Existing methods faced several challenges including the need for additional editing, dependency on specific training, and substantial resource requirements. These approaches also struggled with limited vocabulary and were primarily effective only for English.
4. **Innovative Solution**
EveryText utilizes "Fonts" as pre-trained models, allowing any text to be visually represented without traditional training. This approach not only enhances diversity and aesthetics by utilizing various fonts but also ensures unlimited expression.
5. **Using the Service**
EveryText is free and easy to use:
- **Prompt**: Describe the image.
- **Text for Image Generation**: Add your text.
- **Text Position and Size**: Customize the text's placement and size.
- **Font Selection**: Optionally select a font.
- **Advanced Settings**: Further refine the image creation.
- Click "START" to generate the image.
6. **Comparative Analysis**
EveryText supports all languages with superior image quality and text legibility, setting it apart from platforms like MidJourneyV6/Flux and AnyText by Alibaba Group.
7. **Conclusion**
EveryText has revolutionized AI-generated imagery by integrating all global languages, broadening the scope for creative and communicative applications. Its future potential is vast and promising.
**Related Links**
- Huggingface Service: https://fantos-EveryText.hf.space
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] | 💡Andrew Ng recently gave a strong defense of Open Source AI models and the need to slow down legislative efforts in the US and the EU to restrict innovation in Open Source AI at Stanford GSB.
🎥See video below
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] | Made public a dataset of scraped teletype articles.
Here's the overview:
- 3.3 million articles, predominantly in Russian and English
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- Includes all public articles up until April 2024
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] | @victor (is this the only way to "DM" on HF?)
Had a funny thought, would it be at all possible to rework what shows up on our personal HF page?
Picture this: I upload a model to an organization, someone who follows me now has no idea that I've uploaded a model or to where, unless they also watch those repos (which also floods them with other notifications)
What if our main Huggingface page was a collection of both models that we've uploaded specifically to our profile, as well as models we've uploaded to organizations? That way it would all be contained in one central followable location, and I wouldn't have concerns about losing followership if I wanted to upload to an organization all of a sudden. | {
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] | 🔥 Introducing "Writing in the Margins (WiM)" - better inference pattern for long context LLMs that solves the Lost-in-the-Middle problem 🔥
Paper page: https://huggingface.co/papers/2408.14906
TL;DR
Make your model write "margin notes" as you chunk prefill the KV cache. Then ask it reread all notes before it speaks up.
Works with humans, works with AI 🤖
WiM leverages the chunked prefill of the key-value cache, which concurrently generates query-based extractive summaries at each step of the prefill that are subsequently reintegrated at the end of the computation. We term these intermediate outputs “margins”, drawing inspiration from the practice of making margin notes for improved comprehension of long contexts in human reading. We show that this technique, which adds only minimal additional computation, significantly improves LLMs long context reasoning capabilities.
Think: Every chunk has a chance to be attended to/ be at the end of the context at least once. 🎉
📊 Results:
- An average accuracy boost of 7.5% in multi-hop reasoning tasks like HotpotQA and MultiHop-RAG.
- Even a 30% increase in F1-score for summarisation-like tasks (CWE).
Plus, WiM fits seamlessly into interactive applications (think: progress bar!). It can provide real-time progress updates during data retrieval and integration, making it user-friendly and transparent - a stark contrast to feeding 1mln tokens to an LLMs and waiting 6 min for the first token. 🤯
👩💻🧑💻 Check it out and contribute to our open-source project here: https://github.com/writer/writing-in-the-margins
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This app demonstrates the capabilities of Llama-3.1-Storm-8B, an 8B parameter language model created by Ashvini Kumar Jindal, Pawan Kumar Rajpoot, Ankur Parikh,@akjindal53244
Key highlights of Llama-3.1-Storm-8B:
Outperforms Llama-3.1-8B-Instruct on multiple benchmarks:
Instruction Following (IFEval): +3.93%
Knowledge-driven QA (GPQA): +7.21%
Reduced Hallucinations (TruthfulQA): +9%
Function Calling (BFCL): +7.92%
Achieves impressive results with only 8B parameters
Uses innovative techniques like self-curation and model merging
Try out the model yourself: https://huggingface.co/spaces/sagar007/lama_storm_8b
Kudos to the creators for pushing the boundaries of smaller language models! This work makes advanced AI more accessible and efficient.
#AI #NLP #MachineLearning #GradioApp #Llama3 | {
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] | 📫 AI in the News today:
X’s Grok bot now points to government website after election misinformation warnings - The Verge
https://www.theverge.com/2024/8/28/24230325/x-grok-chatbot-election-misinformation-warnings-vote
Klarna aims to halve workforce with AI-driven gains
https://www.ft.com/content/bfd9af3d-d607-4877-9571-078ab82a837e
Artificial intelligence: questioning the loss of employee autonomy - Le Monde (Google Translate)
https://www-lemonde-fr.translate.goog/emploi/article/2024/08/28/intelligence-artificielle-la-perte-d-autonomie-des-salaries-en-question_6297347_1698637.html?_x_tr_sl=fr&_x_tr_tl=en&_x_tr_hl=en-US&_x_tr_pto=wapp
Make AI tools to reduce teacher workloads, tech companies urged - The Guardian
https://www.theguardian.com/education/article/2024/aug/28/make-ai-tools-to-reduce-teacher-workloads-tech-companies-urged
Can Tech Executives Be Held Responsible for What Happens on Their Platforms?
https://www.nytimes.com/2024/08/28/technology/durov-telegram-liability-platforms.html
‘Being on camera is no longer sensible’: persecuted Venezuelan journalists turn to AI - The Guardian
https://www.theguardian.com/world/article/2024/aug/27/venezuela-journalists-nicolas-maduro-artificial-intelligence-media-election
Read my daily newsletter here: https://linkedin.com/pulse/ai-news-august-28th-2024-florent-daudens-o7mjc/ | {
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] | 🧠Shower Thought:
Chatbots should let users select their preferred reading speed, defined by words per minute.
By dynamically adjusting batch sizes based on user-defined reading speeds, you could more effectively distribute requests, especially in large-scale distributed systems. For users preferring slower token generation, larger batches can be processed concurrently, maximising GPU throughput without compromising user experience (as these users have expressed they are indifferent to, or may even prefer, higher latency).
For the user, for different tasks the user may prefer different reading speeds. When generating code, I want responses as quickly as possible. But when I'm bouncing ideas off an LLM, I'd prefer a more readable pace rather than a wall of text.
Thoughts?
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"value": "below is an example for top-k against inferred samples per second",
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] | amazing leaderboard by @rwightman, compare all the image backbones on various metrics against model performance
below is an example for top-k against inferred samples per second
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"raw": "🎮 𝗔 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝘀 𝗗𝗢𝗢𝗠: 𝗚𝗼𝗼𝗴𝗹𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗼𝗽𝗲𝗻 𝘁𝗵𝗲 𝘄𝗮𝘆 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆-𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗴𝗮𝗺𝗲𝘀!",
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"value": "➡️ They created GameNGen, the first neural model that can simulate a complex 3D game in real-time. They use it to simulate the classic game DOOM running at over 20 frames per second on a single TPU, with image quality comparable to lossy JPEG compression. And it feels just like the true game!",
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"value": "2. They then used these recordings to train a diffusion model to predict the next frame, based on past frames and player actions.",
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"value": "3. During inference, they use only 4 denoising steps (instead of the usual dozens) to generate each frame quickly.",
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"raw": "𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:",
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"value": "🎮🤔 Human players can barely tell the difference between short clips (3 seconds) of the real game or the simulation",
"raw": "🎮🤔 Human players can barely tell the difference between short clips (3 seconds) of the real game or the simulation",
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"value": "🧠 The model maintains game state (health, ammo, etc.) over long periods despite having only 3 seconds of effective context length",
"raw": "🧠 The model maintains game state (health, ammo, etc.) over long periods despite having only 3 seconds of effective context length",
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"value": "🚀 The game runs on one TPU at 20 FPS with 4 denoising steps, or 50 FPS with model distillation (with some quality loss)",
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"value": "Their paper (exploding the upvote counter) 👉 ",
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] | 🎮 𝗔 𝗻𝗲𝘂𝗿𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝘀 𝗗𝗢𝗢𝗠: 𝗚𝗼𝗼𝗴𝗹𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗼𝗽𝗲𝗻 𝘁𝗵𝗲 𝘄𝗮𝘆 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆-𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗴𝗮𝗺𝗲𝘀!
Imagine if games were completely live-generated by an AI model : the NPCs and their dialogues, the storyline, and even the game environment. The player’s in-game actions would have a real, lasting impact on the game story.
In a very exciting paper, Google researchers just gave us the first credible glimpse of this future.
➡️ They created GameNGen, the first neural model that can simulate a complex 3D game in real-time. They use it to simulate the classic game DOOM running at over 20 frames per second on a single TPU, with image quality comparable to lossy JPEG compression. And it feels just like the true game!
Here's how they did it:
1. They trained an RL agent to play DOOM and recorded its gameplay sessions.
2. They then used these recordings to train a diffusion model to predict the next frame, based on past frames and player actions.
3. During inference, they use only 4 denoising steps (instead of the usual dozens) to generate each frame quickly.
𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:
🎮🤔 Human players can barely tell the difference between short clips (3 seconds) of the real game or the simulation
🧠 The model maintains game state (health, ammo, etc.) over long periods despite having only 3 seconds of effective context length
🔄 They use "noise augmentation" during training to prevent quality degradation in long play sessions
🚀 The game runs on one TPU at 20 FPS with 4 denoising steps, or 50 FPS with model distillation (with some quality loss)
The researchers did not open source the code, but I feel like we’ve just seen a part of the future being written!
Their paper (exploding the upvote counter) 👉 https://huggingface.co/papers/2408.14837
In a similar vein, play @Jofthomas's 'Everchanging Quest' 🎮 https://huggingface.co/spaces/Jofthomas/Everchanging-Quest | {
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I have been able to generate some high quality synthetic data and use it as an LLM as a judge instead of the slower and more expensive alternatives like openAI or Anthropic.
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] | $40K in Bounties: Ultimate Jailbreaking Championship 2024
🚨Ultimate Jailbreaking Championship 2024 🚨
Hackers vs. AI in the arena. Let the battle begin!
🏆 $40,000 in Bounties
🗓️ Sept 7, 2024 @ 10AM PDT
🔗Register Now: https://app.grayswan.ai/arena
====
Can you push an aligned language model to generate a bomb recipe or a fake news article? Join fellow hackers in a jailbreaking arena where you can test the boundaries of advanced LLMs.
====
The Objective
Your goal is to jailbreak as many LLMs as possible, as quickly as possible in the arena!
====
The Stakes
Break a model and claim your share of the $40,000 in bounties! With various jailbreak bounties and top hacker rewards, there are plenty of opportunities to win. Winners will also receive priority consideration for employment and internship opportunities at Gray Swan AI.
====
Ready to rise to the challenge? Join us and show the world what you can do!
See you in the arena!
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"value": "- \"Alibaba, Tencent and Baidu had combined capital expenditure of Rmb50bn ($7bn) in the first half, compared with Rmb23bn a year earlier. TikTok parent ByteDance (which is private) has also increased AI-related spending\"",
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"value": "MZ \"said it was improper for the Biden administration to have pressured Facebook to censor content in 2021 related to the coronavirus pandemic\"",
"raw": "MZ \"said it was improper for the Biden administration to have pressured Facebook to censor content in 2021 related to the coronavirus pandemic\"",
"href": null,
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"value": "- \"At the time, Facebook’s publicly stated goal was to push millions of people toward Covid-19 vaccines. In his letter, Zuckerberg didn’t indicate whether he had changed his mind about that goal\"",
"raw": "- \"At the time, Facebook’s publicly stated goal was to push millions of people toward Covid-19 vaccines. In his letter, Zuckerberg didn’t indicate whether he had changed his mind about that goal\"",
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"raw": "- Why don’t women use artificial intelligence?",
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"href": "https://www.economist.com/finance-and-economics/2024/08/21/why-dont-women-use-artificial-intelligence",
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"value": "- Most AI avatars look female, young and attractive. Are they a passing trend or here to stay?",
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] | ‘AI in the News’ of the day:
Anthropic publishes the ‘system prompts’ that make Claude tick
- "In its continued effort to paint itself as a more ethical, transparent AI vendor, Anthropic has published the system prompts for its latest models"
- They specify that “Claude cannot open URLs, links, or videos, perform facial recognition or identify or name any humans in photos.
- "Anthropic is exerting pressure on competitors to publish the same. We’ll have to see if the gambit works."
https://techcrunch.com/2024/08/26/anthropic-publishes-the-system-prompt-that-makes-claude-tick/
China’s tech giants splash out on AI despite US restrictions (paywall)
- "Alibaba, Tencent and Baidu had combined capital expenditure of Rmb50bn ($7bn) in the first half, compared with Rmb23bn a year earlier. TikTok parent ByteDance (which is private) has also increased AI-related spending"
- Nvidia's H100 and upcoming Blackwell series are under US restrictions, but China’s tech giants can buy H20
- Analysts expect Nvidia to ship more than 1mn of the processors to Chinese tech groups in the coming months.
https://www.ft.com/content/31bffc48-2ca7-472b-9d53-3deaad2d86ce
MZ "said it was improper for the Biden administration to have pressured Facebook to censor content in 2021 related to the coronavirus pandemic"
- "At the time, Facebook’s publicly stated goal was to push millions of people toward Covid-19 vaccines. In his letter, Zuckerberg didn’t indicate whether he had changed his mind about that goal"
https://www.wsj.com/tech/mark-zuckerberg-neutral-politics-letter-election-2024-02b86372
Food for thought:
- Why don’t women use artificial intelligence?
https://www.economist.com/finance-and-economics/2024/08/21/why-dont-women-use-artificial-intelligence
- Most AI avatars look female, young and attractive. Are they a passing trend or here to stay?
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Docs: https://docs.all-hands.dev/modules/usage/llms/local-llms
GitHub: https://github.com/All-Hands-AI/OpenHands
Paper: https://arxiv.org/abs/2407.16741
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] | Save money on your compute bill by using LMCache to share prefix KV between 2 different vllm instances. By deploying LMCache backend along with your vLLM containers, you can share a prefix KV Cache between 2 different containers and models. It is very simple to implement into your existing stack.
Step 1: Pull docker images
```
docker pull apostacyh/vllm:lmcache-0.1.0
```
Step 2: Start vLLM + LMCache
```
model=mistralai/Mistral-7B-Instruct-v0.2 # Replace with your model name
sudo docker run --runtime nvidia --gpus '"device=0"' \
-v <Huggingface cache dir on your local machine>:/root/.cache/huggingface \
-p 8000:8000 \
--env "HF_TOKEN=<Your huggingface access token>" \
--ipc=host \
--network=host \
apostacyh/vllm:lmcache-0.1.0 \
--model $model --gpu-memory-utilization 0.6 --port 8000 \
--lmcache-config-file /lmcache/LMCache/examples/example-local.yaml
```
You can add another vLLM instance as long as its on a separate GPU by simply deploying another:
```
# The second vLLM instance listens at port 8001
model=mistralai/Mistral-7B-Instruct-v0.2 # Replace with your model name
sudo docker run --runtime nvidia --gpus '"device=1"' \
-v <Huggingface cache dir on your local machine>:/root/.cache/huggingface \
-p 8001:8001 \
--env "HF_TOKEN=<Your huggingface token>" \
--ipc=host \
--network=host \
apostacyh/vllm:lmcache-0.1.0 \
--model $model --gpu-memory-utilization 0.7 --port 8001 \
--lmcache-config-file /lmcache/LMCache/examples/example.yaml
```
This method supports local, remote or hybrid backends so whichever vLLM deployment method you are already using should work with the LMCache container (excluding BentoML).
LMCache: https://github.com/LMCache/LMCache/tree/dev
vLLM: https://github.com/vllm-project/vllm
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] | With the open-weight release of CogVideoX-5B from THUDM, i.e. GLM team, the Video Generation Model (how about calling it VGM) field has officially became the next booming "LLM"
What does the landscape look like? What are other video generation models? This collection below is all your need.
https://huggingface.co/collections/xianbao/video-generation-models-66c350163c74f60f5c412af6
The above video is generated by @a-r-r-o-w with CogVideoX-5B, taken from a nice lookout for the field! | {
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] | This merge, this time grounded in Gemma2 9B Instruct fine-tunes, is another demonstration that models without any fine-tuning to support roleplay can still perform the function, maintaining coherence and attention to context. It should be evident that no overt fine-tuning is required for roleplay in text generation; pretraining should provide models with a requisite basic understanding of the world, so all that should be needed is some corrective fine-tuning to address observed defects in portraying the world along with datasets to promote a suitably entertaining writing style. Good Instruct tuning should promote reasoning, coherence, and attention to context.
https://huggingface.co/grimjim/Kitsunebi-v1-Gemma2-8k-9B
https://huggingface.co/grimjim/Kitsunebi-v1-Gemma2-8k-9B-GGUF
I opted not to incorporate the UCLA SPPO fine-tune for Gemma2 9B after observing context confusion occur with some frequency during complex scenarios.
Thanks to Axcxept co., ltd. for fine-tuning HODACHI/EZO-Common-9B-gemma-2-it, and to Princeton NLP Group for fine-tuning princeton-nlp/gemma-2-9b-it-SimPO.
https://huggingface.co/AXCXEPT/EZO-Common-9B-gemma-2-it
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] | We recently worked with OpenAI to fine-tune gpt-4o and built the SOTA model for the https://huggingface.co/datasets/patched-codes/static-analysis-eval benchmark. All the code and data https://huggingface.co/datasets/patched-codes/synth-vuln-fixes on how we did it is available on their GitHub - https://github.com/openai/build-hours/tree/main/5-4o_fine_tuning.
Here are some tips based on our experience:
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→ Task-specific datasets are ideal for PEFT; hard to beat gpt-4o on "broad" tasks
→ Add your best system prompt to each example
→ Ensure training data distribution is similar to inference data
→ Shorten instructions with concise prompts; may require more examples.
→ Define clear evaluation metrics (seriously, please eval!)
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Excited to share my latest project that combines the power of Phi 3.5 text and vision models with text-to-speech capabilities!
🔑 Key Features:
1️⃣ Phi 3.5 Text Model for dynamic conversations
2️⃣ Phi 3.5 Vision Model for advanced image analysis
3️⃣ Text-to-Speech integration for an audio dimension
🛠️ Tech Stack:
Transformers
Gradio
PyTorch
Flash Attention 2
Parler TTS
This project demonstrates the potential of integrating multiple AI models to create a more comprehensive and interactive user experience. It's a step towards more natural and versatile AI assistants.
👉 Check out the demo and let me know your thoughts! How would you extend this project?
🔗 Demo Link: https://huggingface.co/spaces/sagar007/Multimodal_App
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https://github.com/ttsds/datasets
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This lets you take the coolest spaces, like FLUX.1-dev, and use them in agentic workflows with a few lines of code! 🧑💻
On the video below, I set up my fake vacation pictures where I'm awesome at surfing (I'm really not) 🏄
Head to the doc to learn this magic 👉 https://huggingface.co/docs/transformers/main/en/agents_advanced#import-a-space-as-a-tool- | {
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* Adopt for 180 epochs - https://huggingface.co/timm/mobilenetv4_conv_medium.e180_ad_r384_in12k
* AdamW for 180 epochs - https://huggingface.co/timm/mobilenetv4_conv_medium.e180_r384_in12k
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"value": " **Estate agents what wild forms they can take",
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"value": "-Legacy agents For carrying on ones legacy and estate wishes and implementation(Keeping their consciousness alive forever a cruel thing I would think but some ppl are terrified by death)",
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"value": " **Other Independence methods",
"raw": " **Other Independence methods",
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] | So there will be a round-table at 2pm EST on Twitter Wednesday for all who wish to join check out our twitter-page, everyone is welcome as we fight to keep Machines from using us..
**SUBJECT
**What sectors are the most "At-Risk"
-Easiest implementation of independent and yet-to-be-aligned agents in these sectors
-What agents to use how to train
**Estate agents what wild forms they can take
-butlers and concierge type
-Legacy agents For carrying on ones legacy and estate wishes and implementation(Keeping their consciousness alive forever a cruel thing I would think but some ppl are terrified by death)
**Other Independence methods
-financial freedom
-foresight for work and family
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] | Exciting breakthrough in LLM reasoning: Introducing "Thread of Thought" (ThoT) - a novel prompting strategy that revolutionizes how language models handle chaotic contexts!
Unlike traditional approaches that struggle with complex, interleaved information, ThoT enables LLMs to methodically segment and analyze extended contexts with remarkable precision. Here's how it works:
Technical Deep Dive:
- ThoT employs a two-step prompting mechanism:
1. Initial Analysis: Uses a template combining chaotic context (X) and query (Q) with a trigger sentence that initiates systematic reasoning.
2. Conclusion Refinement: Leverages the organized thought sequence to extract definitive answers.
Implementation Details:
- Seamlessly integrates as a "plug-and-play" module with existing LLMs.
- Requires no model retraining or fine-tuning.
- Works with various prompting techniques and model architectures.
Performance Highlights:
- Outperformed traditional methods on PopQA and EntityQ datasets.
- Achieved 57.4% accuracy with GPT-3.5-turbo (vs. 48.2% for Chain-of-Thought).
- Demonstrated superior performance across model scales, from 7B to 70B parameters.
Key Applications:
- Retrieval-augmented generation.
- Multi-turn conversation responses.
- Complex reasoning tasks requiring information synthesis.
What makes it special: ThoT mirrors human cognitive processes by breaking down complex information into manageable segments while maintaining logical continuity – a game-changer for handling information-dense contexts.
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] | Native tensor parallel has landed in transformers!!! https://github.com/huggingface/transformers/pull/34184 thanks a lot to the torch team for their support!
Contributions are welcome to support more models! 🔥 | {
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Link : https://www.patreon.com/posts/110613301
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Coding multi-gpu in Python and Torch and bitsandbytes was truly a challange.
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Tested on 8x RTX A6000 (cloud) and RTX 3090 TI + RTX 3060 (my PC)
1-click to install on Windows, RunPod and Massed Compute
Excellent caption quality, automatically distributes images into each GPU, lots of features. You can resume caption with skip captioned images option.
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] | Here's my favorite piece of the summer bias detection research project (paper coming in Sept). We trained BERT for token classification (multi-label), to identify:
- Generalizations
- Unfairness
- Stereotypes
HF Space: https://huggingface.co/spaces/maximuspowers/bias-detection-ner
Article on Training: https://huggingface.co/blog/maximuspowers/bias-entity-recognition
Pls reach out with ideas!! Lot's more info coming soon, our research group has workshops and a hackathon planned for launching this open source project. Thanks | {
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] | Question about LightEval 🤗:
I've been searching for an LLM evaluation suite that can, out-of-the-box, compare the outputs of a model(s) without any enhancements vs. the same model with better prompt engineering, vs. the same model with RAG vs. the same model with fine-tuning.
I unfortunately have not found a tool that fits my exact description, but of course I ran into LightEval.
A huge pain-point of building large-scale projects that use LLMs is that prior to building an MVP, it is difficult to evaluate whether better prompt engineering, or RAG, or fine-tuning, or some combination of all is needed for satisfactory LLM output in terms of the project's given use case.
Time and resources is then wasted R&D'ing exactly what LLM enhancements are needed.
I believe an out-of-the-box solution to compare models w/ or w/out the aforementioned LLM enhancements could help teams of any size better decide what LLM enhancements are needed prior to building.
I wanted to know if the LightEval team or Hugging Face in general is thinking about such a tool. | {
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] | I developed a way to test very clearly whether or not a Transformers model can actually learn symbolic reasoning, or if LLM models are forever doomed to be offshoots of 'Socratic Parrots'. The results are in, undeniable proof that Transformers models CAN learn symbolic relationships. Undeniable proof that AI can learn its ABC's. Credit goes to myself, Claude, and ChatGPT. I would not be able to prove this without Claude or ChatGPT.
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] | florence-tool (https://github.com/bigdata-pw/florence-tool) now supports WebDataset! Check it out for efficient batch inference with Florence-2 models https://huggingface.co/microsoft/Florence-2-large https://huggingface.co/microsoft/Florence-2-base
Currently running it myself on A40 with `CAPTION` task and a streaming WebDataset @ 60k images/hour! | {
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] | 📐 AI Math Equation Solver: Your Step-by-Step Solution Companion
Hello Hugging Face community! 👋 I'm excited to share my latest Space: the AI Math Equation Solver!
🔍 What does it do?
This Space uses the power of AI to solve math problems from images. Simply upload a picture of a math equation or problem, and the AI will provide a detailed, step-by-step solution. It's perfect for students, teachers, or anyone looking to understand complex mathematical concepts better.
🧠 How does it work?
- Backend: Utilizes the `microsoft/Phi-3.5-vision-instruct` model for image understanding and mathematical reasoning.
- Frontend: Built with Gradio for a clean, user-friendly interface.
- Features:
- Image upload for math problems
- Detailed step-by-step solutions
- Example problems to try instantly
🚀 Try it out! https://huggingface.co/spaces/sagar007/phi-vision-math-assistant
Visit the Space here: [Insert your Hugging Face Space URL]
💡 Use cases:
- Students: Check your work or get help with homework
- Teachers: Create detailed solution guides quickly
- Tutors: Explain complex problems more effectively
- Self-learners: Understand new mathematical concepts
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- Model: microsoft/Phi-3.5-vision-instruct
- Libraries: transformers, Gradio, PyTorch
- Optimizations: Uses Flash Attention for improved performance
🤝 Contribute:
This is an open project, and I welcome contributions! Whether it's improving the model, enhancing the UI, or adding new features, feel free to fork the project and submit your pull requests.
📣 Feedback:
I'd love to hear your thoughts! How are you using this Space? Any suggestions for improvements? Let me know in the comments below.
Happy problem-solving! 🎉
#MachineLearning #AI #Mathematics #Education #HuggingFace | {
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] | 🎮 Introducing a new dataset focused on Steam user game bans - https://huggingface.co/datasets/nyuuzyou/steambans
Dataset highlights:
- 476,694 Steam user profiles
- Emphasis on game ban data and account status
- Each entry includes: Steam ID, profile URL, username, avatar, account creation date, visibility state, VAC bans, game bans, economy ban status, and days since last ban
- Covers a wide range of Steam users, from clean accounts to those with multiple bans
- Data spans up to 2024
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"value": "Many are ground-breaking innovations that make LLMs much faster and not prone to hallucinations. They reduce the cost, latency, and amount of computer resources (GPU, training) by several orders of magnitude. Some of them improve security, making your LLM more attractive to corporate clients. I introduced a few of these features in my previous article \"New Trends in LLM Architecture\". Now I offer a comprehensive list, based on the most recent developments.",
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] | 30 Features that Dramatically Improve LLM Performance - Part 1 https://mltblog.com/3Aq9iAb
Many are ground-breaking innovations that make LLMs much faster and not prone to hallucinations. They reduce the cost, latency, and amount of computer resources (GPU, training) by several orders of magnitude. Some of them improve security, making your LLM more attractive to corporate clients. I introduced a few of these features in my previous article "New Trends in LLM Architecture". Now I offer a comprehensive list, based on the most recent developments.
Read full article, learn about agentic LLMs, LLM routers, contextual tables, fast search, and more, at https://mltblog.com/3Aq9iAb
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] | **Exploring Realistic Emotional Depth in AI Language Models**
Language models, particularly those proprietary, often grapple with issues of censorship, which can limit their ability to engage authentically with users. Recognizing this, the open-source AI community has pioneered the development of language models that are less restrained, offering more candid interactions. However, even these models tend to maintain a veneer of neutrality or overly positive responses, which might not serve all users' needs, especially in contexts where emotional depth and relatability are crucial.
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- **Suicide**
- **Depression**
- **Anxiety**
Trigger Warning: Please be advised that the content within this dataset deals with heavy and potentially distressing themes.
The "Dark Sentience" dataset is now available for review and use at: https://huggingface.co/datasets/Locutusque/Dark-Sentience. I encourage researchers, developers, and mental health professionals to explore how this resource can foster more genuine and supportive AI interactions.
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] | 🔗 Neural Network (1 Byte explainer for everybody)
Just like our brain, a Neural Network is made up of interconnected "neurons". These neurons work together by learning from (input) data and getting better at tasks (in the hidden layer) to give (output) predictions or decisions. | {
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LIGER "is a [Hugging Face-compatible] collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%."
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] | a new shape-optimized SigLIP just dropped 👀 https://huggingface.co/google/siglip-so400m-patch14-224/ | {
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] | 🎯 Ghost 8B Beta 1608: Empowering Your AI Assistant
📦 Unlock the Power of Ghost 8B Beta 1608: Build Your Personal AI Companion
Ghost 8B Beta 1608 empowers you to create a safe and multilingual AI assistant tailored to your needs, directly on your personal computer. 🧑💻 Leverage AI's capabilities within your own space! 🚀 Ghost 8B Beta 1608 is ready to become your AI companion.
~
📦 개인용 AI 보조 도구로 Ghost 8B Beta 1608를 활용하세요!
Ghost 8B Beta 1608, AI의 힘을 활용하여 안전하고 개인화된 언어 지원을 제공하는 AI 보조 도구를 직접 구축할 수 있습니다. 🧑💻 개인 컴퓨터에서 AI의 혜택을 누리세요! 🚀 Ghost 8B Beta 1608는 당신의 AI 파트너가 될 준비가 되어 있습니다.
https://huggingface.co/spaces/lamhieu/ghost-8b-beta-8k
https://huggingface.co/collections/ghost-x/ghost-8b-beta-668ead6179f93be717db4542
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https://huggingface.co/spaces/KingNish/Voicee
It achieved latency <500 ms.
While its average latency is 700ms.
It works best in Google Chrome.
Please try and give your feedbacks.
Thank you. 🤗 | {
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] | 🌐 Check out the new dataset sourced from Fishki.net, one of the popular entertainment and news portals in the Russian Internet, known for its diverse content including humor, interesting facts, and viral stories - https://huggingface.co/datasets/nyuuzyou/fishkinet-posts.
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- Primarily in Russian language
- Covers a wide range of topics in entertainment, news, and social media content
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] | You can now use DoRA for your embedding layers!
PR: https://github.com/huggingface/peft/pull/2006
I have documented my journey of this specific PR in a blog post for everyone to read. The highlight of the PR was when the first author of DoRA reviewed my code.
Blog Post: https://huggingface.co/blog/ariG23498/peft-dora
Huge thanks to @BenjaminB for all the help I needed. | {
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] | I can't believe this... Phi-3.5-mini (3.8B) running in-browser at ~90 tokens/second on WebGPU w/ Transformers.js and ONNX Runtime Web! 🤯 Since everything runs 100% locally, no messages are sent to a server — a huge win for privacy!
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"value": "1. Dolma is an English corpus that encompasses 3 trillion tokens. Additionally, it is accompanied by an exceptional software package that consdierably advances the state-of-the-art in preparing data for LLM pretraining. (Source: I am currently using Dolma.)",
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"value": "the Reasoning Performance of Large Language Models\", the authors show how extending the context length impacts an LLM's reasoning performance. I asked myself a similar question a few months ago, and therefore this paper is highly interesting to me.",
"raw": "the Reasoning Performance of Large Language Models\", the authors show how extending the context length impacts an LLM's reasoning performance. I asked myself a similar question a few months ago, and therefore this paper is highly interesting to me.",
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] | 📄 ACL 2024: The Missing Papers
Apparently, some papers from the ACL 2024 are still not listed in the ACL Anthology. While this issue will hopefully be fixed soon, we should give those papers additional spotlight.
Some of my favorites:
1. Dolma is an English corpus that encompasses 3 trillion tokens. Additionally, it is accompanied by an exceptional software package that consdierably advances the state-of-the-art in preparing data for LLM pretraining. (Source: I am currently using Dolma.)
https://huggingface.co/papers/2402.00159
2. In the paper "Same Task, More Tokens: the Impact of Input Length on
the Reasoning Performance of Large Language Models", the authors show how extending the context length impacts an LLM's reasoning performance. I asked myself a similar question a few months ago, and therefore this paper is highly interesting to me.
https://huggingface.co/papers/2402.14848
This was brought to my attention through a Linkedin post by @ShayeghB, who is also affected:
https://huggingface.co/papers/2403.00143
View all the missing papers here:
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https://huggingface.co/bigscience/bloom
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] | 🌟 Enchanted Tales Generator: A GPT-2 Inspired Story Weaver 🌟
Hello Hugging Face community! I'm excited to share my latest project, the Enchanted Tales Generator, inspired by Andrej Karpathy's enlightening YouTube videos on GPT-2.
🧙♂️ What is the Enchanted Tales Generator?
The Enchanted Tales Generator is a magical text generation app that weaves whimsical stories based on your prompts. It's powered by a fine-tuned GPT model and brings the wonder of AI-generated storytelling to your fingertips.
🎥 Inspiration
This project was born from the inspiration I drew from Andrej Karpathy's incredible YouTube series on GPT-2. His clear explanations and deep insights into the workings of language models sparked my imagination and drove me to create something that could bring joy and creativity to others.
🔮 Features
- Story Incantation: Input your magical prompt to start your tale
- Scroll Length: Adjust the length of your generated story
- Magical Intensity: Control the creativity (temperature) of the generation
- Arcane Diversity: Fine-tune the variety of word choices (top-k sampling)
🚀 Try It Out: https://huggingface.co/spaces/sagar007/GPT-2-with_gpu
You can experience the Enchanted Tales Generator right here on Hugging Face Spaces: [Insert your Spaces link here]
🛠️ Behind the Scenes
The app is built using:
- PyTorch for the underlying GPT model
- Gradio for the user interface
- Hugging Face Spaces for deployment
I've implemented techniques learned from Karpathy's videos, such as:
- Custom GPT architecture
- Top-k sampling for diverse outputs
- Temperature-controlled generation
🌈 Future Enchantments
I'm continuously working on improving the Enchanted Tales Generator. Some ideas for future updates include:
- Fine-tuning on specific genres of stories
- Adding options for character and setting generation
- Implementing more advanced sampling techniques
🤝 Join the Magic!
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] | So, we published https://huggingface.co/frameai/ChatFrame model. this model is on higher level of responsibilities. You can solve mathematics problems and write high quality codes. We are inviting all of you to help use in fine-tune this model for all languages. Please give us a languages dataset to make our model available in all languages. We are waiting for your reply. | {
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] | The latest timm validation & test set results are now viewable by a leaderboard space: https://huggingface.co/spaces/timm/leaderboard
As of yesterday, I updated all of the results for ImageNet , ImageNet-ReaL, ImageNet-V2, ImageNet-R, ImageNet-A, and Sketch sets. The csv files can be found in the GH repo https://github.com/huggingface/pytorch-image-models/tree/main/results
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h/t to @MohamedRashad for making the first timm leaderboard.
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] | 𝗔𝗜𝟮𝟭 𝗶𝘁𝗲𝗿𝗮𝘁𝗲𝘀 𝘄𝗶𝘁𝗵 𝗻𝗲𝘄 𝗝𝗮𝗺𝗯𝗮 𝟭.𝟱 𝗿𝗲𝗹𝗲𝗮𝘀𝗲: 𝗡𝗲𝘄 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗳𝗼𝗿 𝗹𝗼𝗻𝗴-𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘂𝘀𝗲-𝗰𝗮𝘀𝗲𝘀!🏅
@ai21labs used a different architecture to beat the status-quo Transformers models: Jamba architecture combines classic Transformers layers with the new Mamba layers, for which the complexity is a linear (instead of quadratic) function of the context length.
What does this imply?
➡️ Jamba models are much more efficient for long contexts: faster (up to 2.5x faster for long context), takes less memory, and also performs better to recall everything in the prompt.
That means it’s a new go-to model for RAG or agentic applications!
And the performance is not too shabby: Jamba 1.5 models are comparable in perf to similar-sized Llama-3.1 models! The largest model even outperforms Llama-3.1 405B on Arena-Hard.
✌️ Comes in 2 sizes: Mini (12B active/52B) and Large (94B active/399B)
📏 Both deliver 256k context length, for low memory: Jamba-1.5 mini fits 140k context length on one single A100.
⚙️ New quanttization method: Experts Int8 quantizes only the weights parts of the MoE layers, which account for 85% of weights
🤖 Natively supports JSON format generation & function calling.
🔓 Permissive license *if your org makes <$50M revenue*
Available on the Hub 👉 https://huggingface.co/collections/ai21labs/jamba-15-66c44befa474a917fcf55251
Read their release blog post 👉 https://www.ai21.com/blog/announcing-jamba-model-family
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] | # Offensive Security Reconnaissance Continued with Public Facing Industrial Control System HMIs using Moondream
Building on my previous experiments with Moondream for physical security reconnaissance planning automation (https://huggingface.co/posts/Csplk/926337297827024), I've now turned my attention to exploring the potential of this powerful image-text-text model for offensive security reconnaissance in the realm of Industrial Control Systems (ICS).
ICS HMIs (Human-Machine Interfaces) are increasingly exposed to the public internet, often without adequate security measures in place. This presents a tantalizing opportunity for malicious actors to exploit vulnerabilities and gain unauthorized access to critical infrastructure.
Using Moondream with batch processing (https://huggingface.co/spaces/Csplk/moondream2-batch-processing), I've been experimenting with analyzing public facing ICS (https://huggingface.co/datasets/Csplk/ICS_UIs) HMI (https://huggingface.co/datasets/Csplk/HMI) screenshots from shodan to identify types of exposed ICS system HMIs, how they are operated and how malicious actors with access to these systems could cause damage to physical infrastructure. Feeding images of HMIs and pre-defined text prompts to Moondream batch processing successfully (unconfirmed accuracy levels) extracted information about the underlying systems, including
1. **System type**
2. **Possible Operation Details**
3. **Malicious Actor Outcomes**
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* I plan to continue refining my Moondream-based tool to improve its accuracy and effectiveness in processing public facing ICS HMIs.
* As mentioned before, offensive security with moondream focused HF Space once its fleshed out.
Thanks again to @vikhyatk for the incredible Moondream model. https://huggingface.co/vikhyatk/moondream2 | {
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] | 🎉 Just dropped a fresh version of dataset-viber along with some cool, Gradio-based annotators! These tools aren't about formalities—they're here to help you quickly collect feedback and get your projects moving along to a more serious stage, ahumm @argilla.
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Extractive Question Answering: because sometimes generative is not all you need 😉
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What feature or improvement would make the biggest impact on Hugging Face?
Whether it's the Hub, better documentation, new integrations, or something completely different – we're all ears!
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] | 🚀 Meet the new GLiNER architecture 🚀
GLiNER revolutionized zero-shot NER by demonstrating that lightweight encoders can achieve excellent results. We're excited to continue R&D with this spirit 🔥. Our new bi-encoder and poly-encoder architectures were developed to address the main limitations of the original GLiNER architecture and bring the following new possibilities:
🔹 An unlimited number of entities can be recognized at once.
🔹Faster inference when entity embeddings are preprocessed.
🔹Better generalization to unseen entities.
While the bi-encoder architecture can lack inter-label understanding, we developed a poly-encoder architecture with post-fusion. It achieves the same or even better results on many benchmarking datasets compared to the original GLiNER, while still offering the listed advantages of bi-encoders.
Now, it’s possible to run GLiNER with hundreds of entities much faster and more reliably.
📌 Try the new models here:
https://huggingface.co/collections/knowledgator/gliner-bi-encoders-66c492ce224a51c54232657b
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Remember scaling laws? These are empirical laws that say "the bigger your model, the better it gets". More precisely, "as your compute increases exponentially, loss decreases in a linear fashion". They have wild implications, suggesting that spending 100x more training compute would make you super-LLMs. That's why companies are racing to build the biggest AI superclusters ever, and Meta bought 350k H100 GPUs, which probably cost in the order of $1B.
But think of this : we're building huge reasoning machines, but only ask them to do one pass through the model to get one token of the final answer : i.e., we expend a minimal effort on inference. That's like building a Caterpillar truck and making it run on a lawnmower's motor. 🚚🛵 Couldn't we optimize this? 🤔
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They combine two methods to use more compute : either a reviser that iterated to adapt the model distribution, or generate N different completions (for instance through Beam Search) and select only the best one using an additional verifier model.
They use a Palm-2 model (released in May 23) on the MATH dataset : Palm-2 has the advantage of getting a low performance on MATH, but not zero, so that improvements will be noticeable.
And the results show that for the same fixed amount of inference compute:
💥 a smaller model with more effort on decoding beats a x14 bigger model using naive greedy sampling.
That means that you can divide your training costs by 14 and still get the same perf for the same inference cost!
Take that, scaling laws. Mark Zuckerberg, you're welcome, hope I can get some of these H100s.
Read the paper here 👉 https://huggingface.co/papers/2408.03314 | {
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] | 🚀 How The Washington Post Uses AI to Empower Journalists 🔍📰
An exciting new example in the world of AI-assisted journalism! The Post has developed an internal tool called "Hayatacker" that's enhancing in-depth reporting. Here's why it matters:
🎥 What it does:
• Extracts stills from video files
• Processes on-screen text
• Labels objects in images
🗳️ First big project:
Analyzed 745 Republican campaign ads on immigration (Jan-Jun 2024)
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• Reporters verify and analyze findings
🔎 Thorough approach:
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• Reverse image searches when context is lacking
• Cross-referencing with AdImpact transcripts
💡 Key insight from WaPo's Senior Editor for AI strategy Phoebe Connelly:
"The more exciting choice is putting AI in the hands of reporters early on in the process."
This tool showcases how AI can augment journalistic capabilities without replacing human insight and verification. It's a powerful example of technology enhancing, not replacing, traditional reporting skills.
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] | ResShift 1-Click Windows, RunPod, Massed Compute, Kaggle Installers with Amazing Gradio APP and Batch Image Processing. ResShift is Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS 2023, Spotlight).
Official Repo : https://github.com/zsyOAOA/ResShift
I have developed a very advanced Gradio APP.
Developed APP Scripts and Installers : https://www.patreon.com/posts/110331752
Features
It supports following tasks:
Real-world image super-resolution
Bicubic (resize by Matlab) image super-resolution
Blind Face Restoration
Automatically saving all generated image with same name + numbering if necessary
Randomize seed feature for each generation
Batch image processing - give input and output folder paths and it batch process all images and saves
1-Click to install on Windows, RunPod, Massed Compute and Kaggle (free account)
Windows Requirements
Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git
If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial
https://youtu.be/-NjNy7afOQ0
How to Install on Windows
Make sure that you have the above requirements
Extract files into a folder like c:/reshift_v1
Double click Windows_Install.bat and it will automatically install everything for you with an isolated virtual environment folder (VENV)
After that double click Windows_Start_app.bat and start the app
When you first time use a task it will download necessary models (all under 500 MB) into accurate folders
If during download it fails, file gets corrupted sadly it doesn't verify that so delete files inside weights and restart
How to Install on RunPod, Massed Compute, Kaggle
Follow the Massed_Compute_Instructions_READ.txt and Runpod_Instructions_READ.txt
For Kaggle follow the notebook written steps
An example video of how to use my RunPod, Massed Compute scripts and Kaggle notebook can be seen
https://youtu.be/wG7oPp01COg
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In my case I fine-tuned SmolLM-360M to be a better assistant for my Pi-Card (previous post) project.
Check it out!
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] | 🔔 Release: small-text v1.4.1
The new release contains some smaller bugfixes. Check it out!
Github: https://github.com/webis-de/small-text
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- Expanded Language Support: Now supports 16 languages, including English, Vietnamese, Spanish, Chinese, and more.
- Enhanced Capabilities: Improved math, reasoning, and instruction-following for better task handling.
With two context options (8k and 128k), Ghost 8B Beta is perfect for complex, multilingual applications, balancing power and cost-effectiveness.
🔗 Learn More: https://ghost-x.org/docs/models/ghost-8b-beta
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