Announcing the winners of the Frugal AI Challenge 🌱
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yjernite
authored
3
papers
about 16 hours ago
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
Paper
•
2406.16746
•
Published
In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI
Paper
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2503.16861
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Published
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1
A Different Approach to AI Safety: Proceedings from the Columbia Convening on Openness in Artificial Intelligence and AI Safety
Paper
•
2506.22183
•
Published
Post
3094
🫡 I am the first and only one to like the French Tax Code Dataset
that's it , that's the post
find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet
that's it , that's the post
find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet
- 2 replies
Post
3975
𝗙𝗶𝗿𝘀𝘁 𝗚𝗣𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 𝘄𝗶𝘁𝗵 𝗘𝗨 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲? 🇪🇺
With the release of the EU data transparency template this week, we finally got to see one of the most meaningful artifacts to come out of the AI Act implementation so far (haven't you heard? AI's all about the data! 📊📚)
The impact of the template will depend on how effectively it establishes a minimum meaningful transparency standard for companies that don't otherwise offer any transparency into their handling of e.g. personal data or (anti?-)competitive practices in commercial licensing - we'll see how those play out as new models are released after August 2nd 👀
In the meantime, I wanted to see how the template works for a fully open-source + commercially viable model, so I filled it out for the SmolLM3 - which my colleagues at Hugging Face earlier this month 🤗 ICYMI, it's fully open-source with 3B parameters and performance matching the best similar-size models (I've switched all my local apps from Qwen3 to it, you should too 💡)
Verdict: congrats to the European Commission AI Office for making it so straightforward! Fully open and transparent models remain a cornerstone of informed regulation and governance, but the different organizational needs of their developers aren't always properly accounted for in new regulation. In this case, it took me all of two hours to fill out and publish the template (including reading the guidelines) - so kudos for making it feasible for smaller and distributed organizations 🙌 Definitely a step forward for transparency 🔍
To learn more have a look at:
- The SmolLM3 model: HuggingFaceTB/SmolLM3-3B
- Its filled out Public Summary of Training Content: hfmlsoc/smollm3-eu-data-transparency
- And if you're interested, some previous remarks on regulatory minimum meaningful standards for data disclosure: https://huggingface.co/blog/yjernite/naiac-data-transparency
With the release of the EU data transparency template this week, we finally got to see one of the most meaningful artifacts to come out of the AI Act implementation so far (haven't you heard? AI's all about the data! 📊📚)
The impact of the template will depend on how effectively it establishes a minimum meaningful transparency standard for companies that don't otherwise offer any transparency into their handling of e.g. personal data or (anti?-)competitive practices in commercial licensing - we'll see how those play out as new models are released after August 2nd 👀
In the meantime, I wanted to see how the template works for a fully open-source + commercially viable model, so I filled it out for the SmolLM3 - which my colleagues at Hugging Face earlier this month 🤗 ICYMI, it's fully open-source with 3B parameters and performance matching the best similar-size models (I've switched all my local apps from Qwen3 to it, you should too 💡)
Verdict: congrats to the European Commission AI Office for making it so straightforward! Fully open and transparent models remain a cornerstone of informed regulation and governance, but the different organizational needs of their developers aren't always properly accounted for in new regulation. In this case, it took me all of two hours to fill out and publish the template (including reading the guidelines) - so kudos for making it feasible for smaller and distributed organizations 🙌 Definitely a step forward for transparency 🔍
To learn more have a look at:
- The SmolLM3 model: HuggingFaceTB/SmolLM3-3B
- Its filled out Public Summary of Training Content: hfmlsoc/smollm3-eu-data-transparency
- And if you're interested, some previous remarks on regulatory minimum meaningful standards for data disclosure: https://huggingface.co/blog/yjernite/naiac-data-transparency
Post
694
👋 Hey there folks,
just submitted my plugin idea to the G-Assist Plugin Hackathon by @nvidia . Check it out, it's a great way to use a local SLA model on a windows machine to easily and locally get things done ! https://github.com/NVIDIA/G-Assist
just submitted my plugin idea to the G-Assist Plugin Hackathon by @nvidia . Check it out, it's a great way to use a local SLA model on a windows machine to easily and locally get things done ! https://github.com/NVIDIA/G-Assist
Post
538
🙋🏻♂️ Hey there folks ,
Yesterday , Nvidia released a reasoning model that beats o3 on science, math and coding !
Today you can try it out here : Tonic/Nvidia-OpenReasoning
hope you like it !
Yesterday , Nvidia released a reasoning model that beats o3 on science, math and coding !
Today you can try it out here : Tonic/Nvidia-OpenReasoning
hope you like it !
Post
3276
🙋🏻♂️ Normalize adding compute & runtime traces to your model cards
- 2 replies
Post
491
Who's going to Raise Summit in Paris Tomorrow ?
If you're around , I would love to meet you :-)
If you're around , I would love to meet you :-)
Post
2066
Congrats to the top trending dataset
institutional/institutional-books-1.0 !
This is a fantastic example of large-scale curation of public domain books with intentional governance for AI research and use - definitely recommend checking it out, experimenting with the metadata ( institutional/institutional-books-1.0-metadata), and starting to build on top of it 🤗
This is a fantastic example of large-scale curation of public domain books with intentional governance for AI research and use - definitely recommend checking it out, experimenting with the metadata ( institutional/institutional-books-1.0-metadata), and starting to build on top of it 🤗
Post
683
🙋🏻♂️ hey there folks ,
So every bio/med/chem meeting i go to i always the same questions "why are you sharing a gdrive link with me for this?" and "Do you have any plans to publish your model weights and datasets on huggingface?" and finally i got a good answer today which explains everything :
basically there is some kind of government censorship on this (usa, but i'm sure others too) and they are told they are not allowed as it is considered a "dataleak" which is illegal !!!!
this is terrible ! but the good news is that we can do something about it !
so there is this "call for opinions and comments" here from the NIH (usa) , and here we can make our opinion on this topic known : https://osp.od.nih.gov/comment-form-responsibly-developing-and-sharing-generative-artificial-intelligence-tools-using-nih-controlled-access-data/
kindly consider dropping your opinion and thoughts about this censorship of science , and share this post , link or thoughts widely .
Together maybe we can start to share data and model weights appropriately and openly in a good way 🙏🏻🚀
cc. @cyrilzakka
So every bio/med/chem meeting i go to i always the same questions "why are you sharing a gdrive link with me for this?" and "Do you have any plans to publish your model weights and datasets on huggingface?" and finally i got a good answer today which explains everything :
basically there is some kind of government censorship on this (usa, but i'm sure others too) and they are told they are not allowed as it is considered a "dataleak" which is illegal !!!!
this is terrible ! but the good news is that we can do something about it !
so there is this "call for opinions and comments" here from the NIH (usa) , and here we can make our opinion on this topic known : https://osp.od.nih.gov/comment-form-responsibly-developing-and-sharing-generative-artificial-intelligence-tools-using-nih-controlled-access-data/
kindly consider dropping your opinion and thoughts about this censorship of science , and share this post , link or thoughts widely .
Together maybe we can start to share data and model weights appropriately and openly in a good way 🙏🏻🚀
cc. @cyrilzakka
Post
2538
🙋🏻♂️ Hey there folks ,
Yesterday the world's first "Learn to Vibe Code" application was released .
As vibe coding is the mainstream paradigm , so now the first educational app is there to support it .
You can try it out already :
https://vibe.takara.ai
and of course it's entirely open source, so i already made my issue and feature branch :-) 🚀
Yesterday the world's first "Learn to Vibe Code" application was released .
As vibe coding is the mainstream paradigm , so now the first educational app is there to support it .
You can try it out already :
https://vibe.takara.ai
and of course it's entirely open source, so i already made my issue and feature branch :-) 🚀
Post
3377
Today in Privacy & AI Tooling - introducing a nifty new tool to examine where data goes in open-source apps on 🤗
HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data 📚🔍
That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK 🙌
The app works in three stages:
1. Download all code files
2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1)
3. Summarize the app's main functionality and data journeys (screen 2)
4. Build a Privacy TLDR with those inputs
It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints 🤗
Note that this is a POC, lots of exciting work to do to make it more robust, so:
- try it: yjernite/space-privacy
- reach out to collab: yjernite/space-privacy
HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data 📚🔍
That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK 🙌
The app works in three stages:
1. Download all code files
2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1)
3. Summarize the app's main functionality and data journeys (screen 2)
4. Build a Privacy TLDR with those inputs
It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints 🤗
Note that this is a POC, lots of exciting work to do to make it more robust, so:
- try it: yjernite/space-privacy
- reach out to collab: yjernite/space-privacy
sasha
authored
a
paper
5 months ago
Post
1611
🙋🏻♂️Hey there folks,
Did you know that you can use ModernBERT to detect model hallucinations ?
Check out the Demo : Tonic/hallucination-test
See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard
check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1
and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect
👆🏻if you like this topic please contribute code upstream 🚀
Did you know that you can use ModernBERT to detect model hallucinations ?
Check out the Demo : Tonic/hallucination-test
See here for Medical Context Demo : MultiTransformer/tonic-discharge-guard
check out the model from KRLabs : KRLabsOrg/lettucedect-large-modernbert-en-v1
and the library they kindly open sourced for it : https://github.com/KRLabsOrg/LettuceDetect
👆🏻if you like this topic please contribute code upstream 🚀
-
- 2 replies
Post
857
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KRLabsOrg/lettucedect-large-modernbert-en-v1 from KRLabsOrg.
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg](
KRLabsOrg
)
- **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
Detect hallucinations in answers based on context and questions using ModernBERT with 8192-token context support!
### Model Details
- **Model Name**: [lettucedect-large-modernbert-en-v1]( KRLabsOrg/lettucedect-large-modernbert-en-v1)
- **Organization**: [KRLabsOrg](
KRLabsOrg
) - **Github**: [https://github.com/KRLabsOrg/LettuceDetect](https://github.com/KRLabsOrg/LettuceDetect)
- **Architecture**: ModernBERT (Large) with extended context support up to 8192 tokens
- **Task**: Token Classification / Hallucination Detection
- **Training Dataset**: [RagTruth]( wandb/RAGTruth-processed)
- **Language**: English
- **Capabilities**: Detects hallucinated spans in answers, provides confidence scores, and calculates average confidence across detected spans.
LettuceDetect excels at processing long documents to determine if an answer aligns with the provided context, making it a powerful tool for ensuring factual accuracy.
yjernite
authored
a
paper
5 months ago
Article
Announcing the winners of the Frugal AI Challenge 🌱
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and 1 other
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9
Tonic
updated
a
Space
6 months ago
TheoLvs
updated
a
Space
6 months ago