Seen many astonishing results from people posting examples of this model in action.
Aurélien-Morgan CLAUDON
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about 1 hour ago
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Aurelien-Morgan's activity
reacted to
AdinaY's
post with 🤯
about 1 hour ago
Post
2574
Wan2.1 🔥📹 new OPEN video model by Alibaba Wan team!
Model: Wan-AI/Wan2.1-T2V-14B
Demo: Wan-AI/Wan2.1
✨Apache 2.0
✨8.19GB VRAM, runs on most GPUs
✨Multi-Tasking: T2V, I2V, Video Editing, T2I, V2A
✨Text Generation: Supports Chinese & English
✨Powerful Video VAE: Encode/decode 1080P w/ temporal precision
Model: Wan-AI/Wan2.1-T2V-14B
Demo: Wan-AI/Wan2.1
✨Apache 2.0
✨8.19GB VRAM, runs on most GPUs
✨Multi-Tasking: T2V, I2V, Video Editing, T2I, V2A
✨Text Generation: Supports Chinese & English
✨Powerful Video VAE: Encode/decode 1080P w/ temporal precision
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- 1 reply
reacted to
freddyaboulton's
post with 🤗🔥
2 days ago
Post
2819
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.
Check out our org: hf.co/fastrtc
That's where FastRTC comes in! It makes WebRTC and Websocket streams super easy with minimal code and overhead.
Check out our org: hf.co/fastrtc
reacted to
lysandre's
post with ❤️
5 days ago
Post
5237
SmolVLM-2 and SigLIP-2 are now part of
They're added on top of the v4.49.0 release, and can be installed from the following tags:
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.
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.
- 1 reply
reacted to
jsulz's
post with 🚀❤️
7 days ago
Post
3286
Time flies!
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:
🤖 We've successfully completed the first migrations from LFS -> Xet to test the infrastructure and prepare for a wider release
✅ 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!)
👀 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 👇
⏩ ⏩ ⏩ Blazing uploads and downloads coming soon. W’re gearing up for a full integration with the Hub's Python library that will make building on the Hub faster than ever - special thanks to @celinah and @Wauplin for their assistance.
🎉 Want Early Access? If you’re curious and want to test it out the bleeding edge that will power the development experience on the Hub, we’d love to partner with you. Let me know!
This is the culmination of a lot of effort from the entire team. Big round of applause to @sirahd @brianronan @jgodlewski @hoytak @seanses @assafvayner @znation @saba9 @rajatarya @port8080 @yuchenglow
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:
🤖 We've successfully completed the first migrations from LFS -> Xet to test the infrastructure and prepare for a wider release
✅ 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!)
👀 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 👇
⏩ ⏩ ⏩ Blazing uploads and downloads coming soon. W’re gearing up for a full integration with the Hub's Python library that will make building on the Hub faster than ever - special thanks to @celinah and @Wauplin for their assistance.
🎉 Want Early Access? If you’re curious and want to test it out the bleeding edge that will power the development experience on the Hub, we’d love to partner with you. Let me know!
This is the culmination of a lot of effort from the entire team. Big round of applause to @sirahd @brianronan @jgodlewski @hoytak @seanses @assafvayner @znation @saba9 @rajatarya @port8080 @yuchenglow
- 1 reply
reacted to
fdaudens's
post with ❤️
7 days ago
Post
5762
🎯 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.
Check it out: perplexity-ai/r1-1776
Blog post: https://perplexity.ai/hub/blog/open-sourcing-r1-1776
Check it out: perplexity-ai/r1-1776
Blog post: https://perplexity.ai/hub/blog/open-sourcing-r1-1776
- 1 reply
Real' interesting.
reacted to
AdinaY's
post with 🔥
8 days ago
Post
2420
The latest paper of DeepSeek is now available on the Daily Papers page 🚀
You can reach out to the authors directly on this page👇
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2502.11089)
You can reach out to the authors directly on this page👇
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2502.11089)
- 1 reply
reacted to
merve's
post with ❤️🚀
8 days ago
Post
5136
Google just released PaliGemma 2 Mix: new versatile instruction vision language models 🔥
> Three new models: 3B, 10B, 28B with res 224, 448 💙
> Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯
Read more https://huggingface.co./blog/paligemma2mix
Try the demo google/paligemma2-10b-mix
All models are here google/paligemma-2-mix-67ac6a251aaf3ee73679dcc4
> Three new models: 3B, 10B, 28B with res 224, 448 💙
> Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯
Read more https://huggingface.co./blog/paligemma2mix
Try the demo google/paligemma2-10b-mix
All models are here google/paligemma-2-mix-67ac6a251aaf3ee73679dcc4
reacted to
fdaudens's
post with ❤️
15 days ago
Post
2681
⭐️ 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.
👉 leaderboard: https://lnkd.in/esrSxetj
👉 blog post: https://lnkd.in/eFJvzHi8
Huge work led by @sasha with @bgamazay @yjernite @sarahooker @regisss @meg
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.
👉 leaderboard: https://lnkd.in/esrSxetj
👉 blog post: https://lnkd.in/eFJvzHi8
Huge work led by @sasha with @bgamazay @yjernite @sarahooker @regisss @meg
- 1 reply
Those guys ship !
reacted to
AdinaY's
post with 🔥😎
15 days ago
Post
3547
InspireMusic 🎵🔥 an open music generation framework by Alibaba FunAudio Lab
Model: FunAudioLLM/InspireMusic-1.5B-Long
Demo: FunAudioLLM/InspireMusic
✨ Music, songs, audio - ALL IN ONE
✨ High quality audio: 24kHz & 48kHz sampling rates
✨ Long-Form Generation: enables extended audio creation
✨ Efficient Fine-Tuning: precision (BF16, FP16, FP32) with user-friendly scripts
Model: FunAudioLLM/InspireMusic-1.5B-Long
Demo: FunAudioLLM/InspireMusic
✨ Music, songs, audio - ALL IN ONE
✨ High quality audio: 24kHz & 48kHz sampling rates
✨ Long-Form Generation: enables extended audio creation
✨ Efficient Fine-Tuning: precision (BF16, FP16, FP32) with user-friendly scripts
-
- 1 reply
reacted to
Kseniase's
post with ❤️
26 days ago
Post
4886
8 Free Sources on Reinforcement Learning
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:
1. "Reinforcement Learning: An Introduction" book by Richard S. Sutton and Andrew G. Barto -> https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
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.
3. OpenAI Spinning Up in Deep RL -> https://spinningup.openai.com/en/latest/index.html
A comprehensive overview of RL with many useful resources
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.
5. RL Course by David Silver (Google DeepMind) -> https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPeb
Many recommend these video lectures as a good foundation
6. RL theory seminars -> https://sites.google.com/view/rltheoryseminars/home?authuser=0
Provides virtual seminars from different experts about RL advancements
7. "Reinforcement Learning Specialization" (a 4-course series on Coursera) -> https://www.coursera.org/learn/fundament
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
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:
1. "Reinforcement Learning: An Introduction" book by Richard S. Sutton and Andrew G. Barto -> https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
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.
3. OpenAI Spinning Up in Deep RL -> https://spinningup.openai.com/en/latest/index.html
A comprehensive overview of RL with many useful resources
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.
5. RL Course by David Silver (Google DeepMind) -> https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPeb
Many recommend these video lectures as a good foundation
6. RL theory seminars -> https://sites.google.com/view/rltheoryseminars/home?authuser=0
Provides virtual seminars from different experts about RL advancements
7. "Reinforcement Learning Specialization" (a 4-course series on Coursera) -> https://www.coursera.org/learn/fundament
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
reacted to
hexgrad's
post with 👍
29 days ago
Post
8368
hexgrad/Kokoro-82M got an upgrade! ⬆️ More voices, more languages,
GitHub: https://github.com/hexgrad/kokoro
PyPI: https://pypi.org/project/kokoro/
Space: hexgrad/Kokoro-TTS
pip install kokoro, and still 82M parameters.GitHub: https://github.com/hexgrad/kokoro
PyPI: https://pypi.org/project/kokoro/
Space: hexgrad/Kokoro-TTS
- 11 replies
reacted to
danielhanchen's
post with ❤️🔥
about 2 months ago
Post
4655
We fixed many bugs in Phi-4 & uploaded fixed GGUF + 4-bit versions! ✨
Our fixed versions are even higher on the Open LLM Leaderboard than Microsoft's!
GGUFs: unsloth/phi-4-GGUF
Dynamic 4-bit: unsloth/phi-4-unsloth-bnb-4bit
You can also now finetune Phi-4 for free on Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb
Read our blogpost for more details on bug fixes etc: https://unsloth.ai/blog/phi4
Our fixed versions are even higher on the Open LLM Leaderboard than Microsoft's!
GGUFs: unsloth/phi-4-GGUF
Dynamic 4-bit: unsloth/phi-4-unsloth-bnb-4bit
You can also now finetune Phi-4 for free on Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb
Read our blogpost for more details on bug fixes etc: https://unsloth.ai/blog/phi4