Getting WebRTC and Websockets right in python is very tricky. If you've tried to wrap an LLM in a real-time audio layer then you know what I'm talking about.
That's where FastRTC comes in! It makes WebRTC and Websocket streams super easy with minimal code and overhead.
SmolVLM-2 and SigLIP-2 are now part of transformers in dedicated releases!
They're added on top of the v4.49.0 release, and can be installed from the following tags: v4.49.0-SmolVLM-2 and v4.49.0-SigLIP-2.
This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).
Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.
Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.
Six months after joining Hugging Face the Xet team is kicking off the first migrations from LFS to our storage for a number of repositories on the Hub.
More on the nitty gritty details behind the migration soon, but here are the big takeaways:
π€ We've successfully completed the first migrations from LFS -> Xet to test the infrastructure and prepare for a wider release
β No action on your part needed - you can work with a Xet-backed repo like any other repo on the Hub (for now - major improvements on their way!)
π Keep an eye out for the Xet logo to see if a repo you know is on our infra! See the screenshots below to spot the difference π
π Want Early Access? If youβre curious and want to test it out the bleeding edge that will power the development experience on the Hub, weβd love to partner with you. Let me know!
π― Perplexity drops their FIRST open-weight model on Hugging Face: A decensored DeepSeek-R1 with full reasoning capabilities. Tested on 1000+ examples for unbiased responses.
Google just released PaliGemma 2 Mix: new versatile instruction vision language models π₯
> Three new models: 3B, 10B, 28B with res 224, 448 π > Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything π€―
βοΈ The AI Energy Score project just launched - this is a game-changer for making informed decisions about AI deployment.
You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI.
Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything.
166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test.
Why this matters: - Teams can pick efficient models that still get the job done - Developers can optimize for energy use from day one - Organizations can finally predict their AI environmental impact
If you're building with AI at any scale, definitely worth checking out.
With the phenomenon of DeepSeek-R1's top reasoning capabilities, we all saw the true power of RL. At its core, RL is a type of machine learning where a model/agent learns to make decisions by interacting with an environment to maximize a reward. RL learns through trial and error, receiving feedback in the form of rewards or penalties.
Here's a list of free sources that will help you dive into RL and how to use it:
2. Hugging Face Deep Reinforcement Learning Course -> https://huggingface.co/learn/deep-rl-course/unit0/introduction You'll learn how to train agents in unique environments, using best libraries, share your results, compete in challenges, and earn a certificate.
4. "Reinforcement Learning and Optimal Control" books, video lectures and course material by Dimitri P. Bertsekas from ASU -> https://web.mit.edu/dimitrib/www/RLbook.html Explores approximate Dynamic Programming (DP) and RL with key concepts and methods like rollout, tree search, and neural network training for RL and more.
8. Concepts: RLHF, RLAIF, RLEF, RLCF -> https://www.turingpost.com/p/rl-f Our flashcards easily explain what are these four RL approaches with different feedback
Community fine-tuned models are more carbon efficient than the models they are derived from! π₯³πΏ
@alozowski@clefourrier@SaylorTwift@albertvillanova evaluated COβ emissions associated with model inference for over 3000 models on the Open LLM Leaderboard. Interesting trends and new insights emerged...π
π Supercharge your LLM apps with Langfuse on Hugging Face Spaces!
Langfuse brings end-to-end observability and tooling to accelerate your dev workflow from experiments through production
Now available as a Docker Space directly on the HF Hub! π€
π Trace everything: monitor LLM calls, retrieval, and agent actions with popular frameworks 1β£ One-click deployment: on Spaces with persistent storage and integrated OAuth π Simple Prompt Management: Version, edit, and update without redeployment β Intuitive Evals: Collect user feedback, run model/prompt evaluations, and improve quality π Dataset Creation: Build datasets directly from production data to enhance future performance
Kudos to the Langfuse team for this collab and the awesome, open-first product theyβre building! π @marcklingen@Clemo@MJannik
