Multimodal OCR: Parse Anything from Documents
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akhaliq
submitted a paper to Daily Papers about 11 hours ago
sergiopaniego
updated a dataset about 13 hours ago
sergiopaniego
posted an
update 3 days ago
Post
207
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
clefourrier
authored a paper 3 days ago
AdinaY
submitted a paper to Daily Papers 3 days ago
hysts
updated a Space 3 days ago
sergiopaniego
posted an
update 4 days ago
Post
156
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 5 days ago
Post
205
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
pcuenq
updated a dataset 5 days ago
AdinaY
submitted a paper to Daily Papers 7 days ago
hysts
updated 7 Spaces 9 days ago
Gemma 3 12b It
🔥
163
Chat with a multimodal AI using images, video, and text
Gemma 2 2B JPN IT
😻
22
Chatbot
Gemma 2 9B IT
😻
101
Chatbot
Gemma 2 2B IT
😻
86
Chatbot
Llama 3.2 3B Instruct
😻
123
Chatbot
Llama 2 13b Chat
🦙
490
Chat with the Llama‑2 13B language model
Llama 2 7B Chat
🏆
482
Chat with an AI using Llama‑2 7B model
sergiopaniego
posted an
update 13 days ago
Post
482
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 14 days ago
Post
368
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 14 days ago