On the Shifting Global Compute Landscape
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sayakpaul
updated
a
dataset
1 day ago
allenai/olmOCR-2-7B-1025
➕
1
#5868 opened 3 days ago
by
yjernite
sergiopaniego
posted
an
update
3 days ago
Post
2834
Sharing the slides from yesterday's talk about "Fine Tuning with TRL" from the
@TogetherAgent
x
@huggingface
workshop we hosted in our Paris office 🎃!
Link: https://github.com/sergiopaniego/talks/blob/main/fine_tuning_with_trl/Fine%20tuning%20with%20TRL%20(Oct%2025).pdf
Link: https://github.com/sergiopaniego/talks/blob/main/fine_tuning_with_trl/Fine%20tuning%20with%20TRL%20(Oct%2025).pdf
sergiopaniego
posted
an
update
5 days ago
sergiopaniego
posted
an
update
11 days ago
Post
2706
Meet OpenEnv 👋, an open ecosystem of environments for intelligent agents. Build, share, and test agents safely and consistently.
Ideal for training with TRL (we include examples🤓), deployment, and community collaboration via the HF Hub
Blog: https://huggingface.co/blog/openenv
Hub for Environments:
openenv
OpenEnv repo: https://github.com/meta-pytorch/OpenEnv
Try it out using TRL: https://huggingface.co/docs/trl/main/en/openenv
Ideal for training with TRL (we include examples🤓), deployment, and community collaboration via the HF Hub
Blog: https://huggingface.co/blog/openenv
Hub for Environments:
openenv
OpenEnv repo: https://github.com/meta-pytorch/OpenEnv
Try it out using TRL: https://huggingface.co/docs/trl/main/en/openenv
- 1 reply
andito
authored
a
paper
12 days ago
Post
1598
Finally, our new paper is out! "𝗙𝗶𝗻𝗲𝗩𝗶𝘀𝗶𝗼𝗻: 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 𝗜𝘀 𝗔𝗹𝗹 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱"! 🥳
FineVision: Open Data Is All You Need (2510.17269)
If you've ever trained a VLM, you know this problem: nobody shares their data mixtures. It's a black box, making replicating SOTA work impossible.
We wanted to change that.
FineVision unifies 200 sources into 24 million samples. With 17.3 million images and 9.5 billion answer tokens, it's the largest open resource of its kind.
In the paper, we share how we built it:
🔍 finding and cleaning data at scale
🧹 removing excessive duplicates across sources
🤗 decontaminating against 66 public benchmarks
My favorite part is Figure 6 (in the video!). It's our visual diversity analysis. It shows that FineVision isn't just bigger; it's more balanced and conceptually richer than other open datasets.
NVIDIA's Eagle 2 paper highlighted just how critical this visual diversity is, and our results confirm it: models trained on FineVision consistently outperform those trained on any other open dataset on 11 benchmarks!
🎉 To celebrate the paper, I’m also releasing a concatenated and shuffled version of the full dataset! 👉
It’s ready to stream, so you can start training your own models right away:
from datasets import load_dataset
d = load_dataset("HuggingFaceM4/FineVision_full_shuffled", split="train", streaming=True)
print(next(iter(d)))
A big shoutout to the first authors: Luis Wiedmann and Orr Zohar. They are rockstars!
FineVision: Open Data Is All You Need (2510.17269)
If you've ever trained a VLM, you know this problem: nobody shares their data mixtures. It's a black box, making replicating SOTA work impossible.
We wanted to change that.
FineVision unifies 200 sources into 24 million samples. With 17.3 million images and 9.5 billion answer tokens, it's the largest open resource of its kind.
In the paper, we share how we built it:
🔍 finding and cleaning data at scale
🧹 removing excessive duplicates across sources
🤗 decontaminating against 66 public benchmarks
My favorite part is Figure 6 (in the video!). It's our visual diversity analysis. It shows that FineVision isn't just bigger; it's more balanced and conceptually richer than other open datasets.
NVIDIA's Eagle 2 paper highlighted just how critical this visual diversity is, and our results confirm it: models trained on FineVision consistently outperform those trained on any other open dataset on 11 benchmarks!
🎉 To celebrate the paper, I’m also releasing a concatenated and shuffled version of the full dataset! 👉
HuggingFaceM4/FineVision_full_shuffled It’s ready to stream, so you can start training your own models right away:
from datasets import load_dataset
d = load_dataset("HuggingFaceM4/FineVision_full_shuffled", split="train", streaming=True)
print(next(iter(d)))
A big shoutout to the first authors: Luis Wiedmann and Orr Zohar. They are rockstars!
ariG23498
authored
a
paper
13 days ago
Post
419
We have released
Headline features:
* 🔮 Supports building kernels for the brand-new PyTorch 2.9.0.
* 🪟 Experimental support for building Windows kernels.
kernel-builder 0.7.0: https://github.com/huggingface/kernel-builder/releases/tag/v0.7.0Headline features:
* 🔮 Supports building kernels for the brand-new PyTorch 2.9.0.
* 🪟 Experimental support for building Windows kernels.
sergiopaniego
posted
an
update
17 days ago
Post
1902
New drop! 💥 The VLM Object Understanding Comparison Space now runs with Qwen3-VL-4B and moondream3.
You can compare how models reason about images 🧠
Bonus: thanks to @ariG23498 , you now get auto-suggested prompts to explore faster.
Let’s gooo
sergiopaniego/vlm_object_understanding
You can compare how models reason about images 🧠
Bonus: thanks to @ariG23498 , you now get auto-suggested prompts to explore faster.
Let’s gooo
sergiopaniego/vlm_object_understanding
sergiopaniego
posted
an
update
17 days ago
Post
858
New drop! 💥 The VLM Object Understanding Comparison Space now runs with Qwen3-VL-4B and moondream3.
You can compare how models reason about images 🧠
Bonus: thanks to @ariG23498 , you now get auto-suggested prompts to explore faster.
Let’s gooo
sergiopaniego/vlm_object_understanding
You can compare how models reason about images 🧠
Bonus: thanks to @ariG23498 , you now get auto-suggested prompts to explore faster.
Let’s gooo
sergiopaniego/vlm_object_understanding
multimodalart
posted
an
update
18 days ago
Post
2213
Want to iterate on a Hugging Face Space with an LLM?
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
thomwolf
authored
a
paper
19 days ago
sergiopaniego
posted
an
update
19 days ago
Post
2288
@Qwen
released their new small and dense VLMs (Qwen3-VL).
They're incredibly capable and one of my all-time favourite VLMs.
🤗 We’ve prepared some resources to help you get started.
> Fine-tune Qwen3-VL-4B with SFT or GRPO (free Colab notebooks):
> SFT: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_qwen_vl.ipynb
> GRPO: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_qwen3_vl.ipynb
> Compare object detection vs. Moondream3:
sergiopaniego/vlm_object_understanding
> Fine-tune from the CLI using TRL:
https://github.com/kashif/Qwen3-VL/blob/trl-sft/qwen-vl-finetune/README.md#trl-based-training-single-gpu
They're incredibly capable and one of my all-time favourite VLMs.
🤗 We’ve prepared some resources to help you get started.
> Fine-tune Qwen3-VL-4B with SFT or GRPO (free Colab notebooks):
> SFT: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_qwen_vl.ipynb
> GRPO: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/grpo_qwen3_vl.ipynb
> Compare object detection vs. Moondream3:
sergiopaniego/vlm_object_understanding
> Fine-tune from the CLI using TRL:
https://github.com/kashif/Qwen3-VL/blob/trl-sft/qwen-vl-finetune/README.md#trl-based-training-single-gpu
lvwerra
authored
a
paper
20 days ago
sergiopaniego
posted
an
update
24 days ago
Post
1459
Super nice intro to fine-tuning with TRL, just dropped by
@google
(runs free on Colab)!
They use SFT + QLoRA to fine-tune the tiny Gemma 3 270M model for emoji generation
Here’s what the fine-tuned model generates for the prompt: “I'm learning to tweet” → 🐦🗣💻
Colab: https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/Demos/Emoji-Gemma-on-Web/resources/Fine_tune_Gemma_3_270M_for_emoji_generation.ipynb
Try it out: google/emoji-gemma
Learn more: https://developers.googleblog.com/en/own-your-ai-fine-tune-gemma-3-270m-for-on-device/
They use SFT + QLoRA to fine-tune the tiny Gemma 3 270M model for emoji generation
Here’s what the fine-tuned model generates for the prompt: “I'm learning to tweet” → 🐦🗣💻
Colab: https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/Demos/Emoji-Gemma-on-Web/resources/Fine_tune_Gemma_3_270M_for_emoji_generation.ipynb
Try it out: google/emoji-gemma
Learn more: https://developers.googleblog.com/en/own-your-ai-fine-tune-gemma-3-270m-for-on-device/
Post
4394
🌎 AI ethics and sustainability are two sides of the same coin.
In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.
Ethical and sustainable AI development can’t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.
We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:
📊 Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as we’ve done with tools like the AI Energy Score.
🔍 Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.
AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.
Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability
AIEnergyScore/Leaderboard
sasha/environmental-transparency
In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.
Ethical and sustainable AI development can’t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.
We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:
📊 Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as we’ve done with tools like the AI Energy Score.
🔍 Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.
AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.
Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability
AIEnergyScore/Leaderboard
sasha/environmental-transparency
- 1 reply
sasha
authored
3
papers
26 days ago
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
Paper
•
2211.02001
•
Published
Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI
Paper
•
2409.14160
•
Published
•
3
From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate
Paper
•
2501.16548
•
Published