State of Open Source on Hugging Face: Spring 2026
•
63
AI & ML interests
The AI community building the future.
Recent Activity
View all activity
Papers
View all Papers Articles
sayakpaul
updated a
dataset about 18 hours ago
Upload 5 files
#20 opened about 23 hours ago
by 
evijit
Upload 33 files
#19 opened about 23 hours ago
by evijit
julien-c
updated a
dataset 1 day ago
[FEEDBACK] Inference Playground 🕹️
🔥🤗 3
5
#12 opened 26 days ago
by 
enzostvs
nielsr
submitted a
paper to Daily Papers 1 day ago
lysandre
updated a
dataset 1 day ago
nielsr
submitted a
paper to Daily Papers 5 days ago
akhaliq
submitted a
paper to Daily Papers 6 days ago
nielsr
submitted a
paper to Daily Papers 6 days ago
akhaliq
submitted a
paper to Daily Papers 9 days ago
sergiopaniego
posted an update 11 days ago
Post
481
ICYMI, great blog by @kashif and @stas on Ulysses Sequence Parallelism: train with million-token contexts
on 4×H100s: 12x longer sequences, 3.7x throughput
learn how to integrate it with Accelerate, Transformers, and TRL ⤵️
https://huggingface.co/blog/ulysses-sp
on 4×H100s: 12x longer sequences, 3.7x throughput
learn how to integrate it with Accelerate, Transformers, and TRL ⤵️
https://huggingface.co/blog/ulysses-sp
nielsr
authored a
paper 12 days ago
sergiopaniego
posted an update 12 days ago
Post
274
We just released a big blog surveying 16 OSS frameworks for async RL training of LLMs!
We're building a new async GRPO trainer for TRL and as first step, we needed to understand how the ecosystem solves this problem today.
The problem: in synchronous RL training, generation dominates wall-clock time. 32K-token rollouts on a 32B model take hours while training GPUs sit completely idle. With reasoning models and agentic RL making rollouts longer and more variable, this only gets worse.
The ecosystem converged on the same fix: separate inference + training onto different GPU pools, rollout buffer, and async weight sync.
We compared 16 frameworks across 7 axes: orchestration, buffer design, weight sync, staleness management, partial rollouts, LoRA, and MoE support.
This survey is step one. The async GRPO trainer for TRL is next!
https://huggingface.co/blog/async-rl-training-landscape
We're building a new async GRPO trainer for TRL and as first step, we needed to understand how the ecosystem solves this problem today.
The problem: in synchronous RL training, generation dominates wall-clock time. 32K-token rollouts on a 32B model take hours while training GPUs sit completely idle. With reasoning models and agentic RL making rollouts longer and more variable, this only gets worse.
The ecosystem converged on the same fix: separate inference + training onto different GPU pools, rollout buffer, and async weight sync.
We compared 16 frameworks across 7 axes: orchestration, buffer design, weight sync, staleness management, partial rollouts, LoRA, and MoE support.
This survey is step one. The async GRPO trainer for TRL is next!
https://huggingface.co/blog/async-rl-training-landscape
sergiopaniego
posted an update 13 days ago
Post
291
Nemotron 3 Super by @nvidia is here! NVIDIA's hybrid Mamba2/Transformer models are now natively supported in transformers (no trust_remote_code needed)
Fine-tune them with TRL in just a few lines of code. Notebook + script included to get started right away. goooo!
- Notebook: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_nemotron_3.ipynb
- Script: https://github.com/huggingface/trl/blob/main/examples/scripts/sft_nemotron_3.py
- Collection with all the models: https://huggingface.co/collections/nvidia/nvidia-nemotron-v3
Fine-tune them with TRL in just a few lines of code. Notebook + script included to get started right away. goooo!
- Notebook: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_nemotron_3.ipynb
- Script: https://github.com/huggingface/trl/blob/main/examples/scripts/sft_nemotron_3.py
- Collection with all the models: https://huggingface.co/collections/nvidia/nvidia-nemotron-v3
alvarobartt
posted an update 19 days ago
Post
3373
Learn how to deploy Microsoft Research VibeVoice ASR on Microsoft Azure Foundry with Hugging Face to generate rich audio transcriptions with Who, When, and What! 💥
> 🕒 60-minute single-pass processing, no chunking or stitching
> 👤 Customized hotwords to guide recognition on domain-specific content
> 📝 Rich transcription: joint ASR + diarization + timestamping in one pass
> 🌍 50+ languages with automatic detection and code-switching support
> 🤗 Deployed on Microsoft Foundry via an OpenAI-compatible Chat Completions API
https://huggingface.co/docs/microsoft-azure/foundry/examples/deploy-vibevoice-asr
> 🕒 60-minute single-pass processing, no chunking or stitching
> 👤 Customized hotwords to guide recognition on domain-specific content
> 📝 Rich transcription: joint ASR + diarization + timestamping in one pass
> 🌍 50+ languages with automatic detection and code-switching support
> 🤗 Deployed on Microsoft Foundry via an OpenAI-compatible Chat Completions API
https://huggingface.co/docs/microsoft-azure/foundry/examples/deploy-vibevoice-asr
sergiopaniego
posted an update 21 days ago
Post
544
did you know you can train agentic models with RL deploying the environments on HF Spaces? 🤗
with TRL + OpenEnv, your training script connects to remote environments hosted as Spaces
want to train faster? → just add more Spaces (TRL handles the parallelization natively)
we used this to train a model to solve the trolley problem in CARLA. 2 HF Spaces running a full driving simulator, each on a T4 GPU
full write-up with code and results → https://huggingface.co/blog/sergiopaniego/bringing-carla-to-openenv-trl
with TRL + OpenEnv, your training script connects to remote environments hosted as Spaces
want to train faster? → just add more Spaces (TRL handles the parallelization natively)
we used this to train a model to solve the trolley problem in CARLA. 2 HF Spaces running a full driving simulator, each on a T4 GPU
full write-up with code and results → https://huggingface.co/blog/sergiopaniego/bringing-carla-to-openenv-trl
sergiopaniego
posted an update 22 days ago
Post
413
Qwen3.5 dense (smol 🤏) models just dropped
- natively multimodal
- 0.8B · 2B · 4B · 9B (+ base variants)
- 262K context extensible to 1M
- built-in thinking
fine-tune them with TRL out of the box → SFT, GRPO, DPO and more!
examples: https://huggingface.co/docs/trl/example_overview
collection: https://huggingface.co/collections/Qwen/qwen35
- natively multimodal
- 0.8B · 2B · 4B · 9B (+ base variants)
- 262K context extensible to 1M
- built-in thinking
fine-tune them with TRL out of the box → SFT, GRPO, DPO and more!
examples: https://huggingface.co/docs/trl/example_overview
collection: https://huggingface.co/collections/Qwen/qwen35
sayakpaul
authored a
paper 22 days ago