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andito  authored a paper 5 days ago
FineVision: Open Data Is All You Need
ariG23498  authored a paper 6 days ago
FineVision: Open Data Is All You Need
andito  updated a dataset 6 days ago
HuggingFaceM4/FineVisionMax
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jbilcke-hf 
posted an update about 2 hours ago
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26
I made a code sniping agent to detect when new AI papers with code (and weights) are released, and then automatically create a Gradio demo on Hugging Face 🧙

Here are some examples generated 100% automatically:
https://huggingface.co/collections/jbilcke-hf/sniped

I call this agent CheatCode (https://github.com/jbilcke-hf/CheatCode) because it skips so many steps that it kinda feels like breaking the rules of the AI tech release game 😅

As with any experimental technology, there is still room for improvement 👩🏻‍🔬:

- Currently the demos are all generated in one go and not built or tested by the agent itself. A more robust version should loop over the deployed app to fix build/runtime issues.
- There is still a bit of human curation done to avoid making demos for things that can’t really be demonstrated on ZeroGPU (eg. tasks taking several minutes)
- Some papers can actually be showcased in a variety of ways, which isn’t really supported (see Demo 2)
andito 
authored a paper 5 days ago
andito 
posted an update 6 days ago
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1535
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! 👉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 6 days ago
andito 
updated a dataset 6 days ago
andito 
published a dataset 6 days ago
andito 
in HuggingFaceM4/FineVisionMax 6 days ago

Feat: Move 9999 root .parquet files to 'full/' directory
#2 opened 6 days ago by
andito
tfrere 
updated a Space 6 days ago
andito 
in HuggingFaceM4/FineVision 6 days ago

Added arxiv
#29 opened 6 days ago by
lusxvr
merve 
posted an update 7 days ago
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4456
deepseek-ai/DeepSeek-OCR is out! 🔥 my take ⤵️
> pretty insane it can parse and re-render charts in HTML
> it uses CLIP and SAM features concatenated, so better grounding
> very efficient per vision tokens/performance ratio
> covers 100 languages
  • 2 replies
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multimodalart 
posted an update 11 days ago
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1513
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
thomwolf 
authored a paper 12 days ago
lvwerra 
authored a paper 13 days ago
andito 
in HuggingFaceM4/FineVision 15 days ago

Upload video_20251001_011427.mp4
#26 opened 27 days ago by
Sachiuii
tfrere 
in HuggingFaceM4/FineVision 16 days ago

Update README.md
#27 opened 20 days ago by
lusxvr
giadap 
posted an update 18 days ago
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4365
🌎 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
  • 1 reply
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sasha 
authored 3 papers 19 days ago
giadap 
posted an update 28 days ago
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10829
One of the hardest challenges in AI safety is finding the right balance: how do we protect people from harm without undermining their agency? This tension is especially visible in conversational systems, where safeguards can sometimes feel more paternalistic than supportive.

In my latest piece for Hugging Face, I argue that open source and community-driven approaches offer a promising (though not exclusive) way forward.

✨ Transparency can make safety mechanisms into learning opportunities.
✨ Collaboration with diverse communities makes safeguards more relevant across contexts.
✨ Iteration in the open lets protections evolve rather than freeze into rigid, one-size-fits-all rules.

Of course, this isn’t a silver bullet. Top-down safety measures will still be necessary in some cases. But if we only rely on corporate control, we risk building systems that are safe at the expense of trust and autonomy.

Read the blog post here: https://huggingface.co/blog/giadap/preserving-agency
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