The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
Paper
•
2406.16746
•
Published
AI & ML interests
✍️ Rigor, 🤝 Consent, 👁️🗨️ Social Consciousness, 🌎 Sustainability, 🧑🤝🧑 Inclusion, and 🤔 Inquisitiveness
Recent Activity
yjernite
authored
3
papers
about 19 hours ago
Post
2588
Thread to gossip during the
openai
GPT-5 livestream: https://www.youtube.com/watch?v=0Uu_VJeVVfo. Feel free to post your impressions below!
openai
GPT-5 livestream: https://www.youtube.com/watch?v=0Uu_VJeVVfo. Feel free to post your impressions below!
- 29 replies
BrigitteTousi
posted
an
update
2 days ago
Post
273
New interactive viz from AI World showing OpenAI's new open model gpt-oss-120b breaking into the top 50 most liked models of all time on the Hub in under a day! ☄️☄️☄️
Post
211
🤖 ICYMI: Yesterday, Hugging Face and OpenAI partnered to bring open source GPT to the public. This is a Big Deal in "AI world".
0. Common ground setting: OpenAI is the ChatGPT people. An “open source” model is one whose weights are available — that means the model can be “yours”.
1. You don’t have to interact with the company directly, nor give them your interactions, to use the system. The company can't "surveil" you.
2. You can evaluate the unique contributions of their SOTA model much more rigorously than you can when there are collections of models+code behind a closed API. You can find out specifically what the model can and can't do.
3. And you can directly customize it for whatever you'd like. Fine-tuning, wherein you give the model data that's tailored to your use cases and train it some more on that data, is trivial* when you have the model weights.
*Provided you have the compute.
4. You can directly benchmark whatever you'd like. Biases? Energy usage? Strengths/weaknesses? Go for it. You wants it you gots it--this transparency helps people understand SOTA *in general*, not just for this model, but points to, e.g., what's going on with closed Google models as well.
5. One of the most powerful things about "openness" that I've learned is that it cultivates ecosystems of collaborators building on top of one another's brilliance to make systems that are significantly better than they would be if created in isolation.
But, caveat wrt my own philosophy...
6. I do not take it as a given that advancing LLMs is good, and have a lot more to say wrt where I think innovation should focus more. For example, a focus on *data* -- curation, measurement, consent, credit, compensation, safety -- would deeply improve technology for everyone.
7. The transparency this release provides is massive for people who want to *learn* about LLMs. For the next generation of technologists to advance over the current, they MUST be able to learn about what's happening now. (cont...)
0. Common ground setting: OpenAI is the ChatGPT people. An “open source” model is one whose weights are available — that means the model can be “yours”.
1. You don’t have to interact with the company directly, nor give them your interactions, to use the system. The company can't "surveil" you.
2. You can evaluate the unique contributions of their SOTA model much more rigorously than you can when there are collections of models+code behind a closed API. You can find out specifically what the model can and can't do.
3. And you can directly customize it for whatever you'd like. Fine-tuning, wherein you give the model data that's tailored to your use cases and train it some more on that data, is trivial* when you have the model weights.
*Provided you have the compute.
4. You can directly benchmark whatever you'd like. Biases? Energy usage? Strengths/weaknesses? Go for it. You wants it you gots it--this transparency helps people understand SOTA *in general*, not just for this model, but points to, e.g., what's going on with closed Google models as well.
5. One of the most powerful things about "openness" that I've learned is that it cultivates ecosystems of collaborators building on top of one another's brilliance to make systems that are significantly better than they would be if created in isolation.
But, caveat wrt my own philosophy...
6. I do not take it as a given that advancing LLMs is good, and have a lot more to say wrt where I think innovation should focus more. For example, a focus on *data* -- curation, measurement, consent, credit, compensation, safety -- would deeply improve technology for everyone.
7. The transparency this release provides is massive for people who want to *learn* about LLMs. For the next generation of technologists to advance over the current, they MUST be able to learn about what's happening now. (cont...)
- 1 reply
Post
403
🤖 👾 Thanks so much to BBC News and the stellar Suranjana Tewari for having me on to talk about US <—> China relationship in AI, and what it means for AI ethics.
christopher
updated
a
Space
10 days ago
Updated addresses of dataset files
1
#4 opened 11 months ago
by
Saeid
Post
2950
💬 From Replika to everyday chatbots, millions of people are forming emotional bonds with AI, sometimes seeking comfort, sometimes seeking intimacy. But what happens when an AI tells you "I understand how you feel" and you actually believe it?
At Hugging Face, together with @frimelle and @yjernite , we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.
Here's what we found:
👉 Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension.
👉 We mapped how leading AI systems actually respond to vulnerable prompts. 👉 We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).
Check out the blog post: https://huggingface.co/blog/giadap/evaluating-companionship
🚢 We also shipped two visualization tools with Gradio to see how different models behave when things get emotionally intense:
- AI-companionship/intima-responses-2D
- giadap/INTIMA-responses
At Hugging Face, together with @frimelle and @yjernite , we dug into something we felt wasn't getting enough attention: the need to evaluate AI companionship behaviors. These are the subtle ways AI systems validate us, engage with us, and sometimes manipulate our emotional lives.
Here's what we found:
👉 Existing benchmarks (accuracy, helpfulness, safety) completely miss this emotional dimension.
👉 We mapped how leading AI systems actually respond to vulnerable prompts. 👉 We built the Interactions and Machine Attachment Benchmark (INTIMA): a first attempt at evaluating how models handle emotional dependency, boundaries, and attachment (with a full paper coming soon).
Check out the blog post: https://huggingface.co/blog/giadap/evaluating-companionship
🚢 We also shipped two visualization tools with Gradio to see how different models behave when things get emotionally intense:
- AI-companionship/intima-responses-2D
- giadap/INTIMA-responses
How to access the dataset ???
2
#5 opened 11 days ago
by
pega0501
Post
3976
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
BrigitteTousi
posted
an
update
15 days ago
Post
510
This is what Hugging Face is all about. We want everyone, hobbyists, researchers and industry alike, to be able to contribute to AI because everyone is affected by it. Kudos to HF's
@irenesolaiman
for spreading the word!🔥🤗
Post
1240
🤖 Technology means power, and whoever owns the technology owns the power.
Thrilled to share insights from my recent interview with MIT Technology Review about the growing movement toward local LLMs and what it means for AI democratization. Read here: https://www.technologyreview.com/2025/07/17/1120391/how-to-run-an-llm-on-your-laptop/
🤔 Why this matters: When we use "free" online AI services, we're often the product. Our conversations become training data, our personal stories get "cooked into" models, and our privacy becomes a commodity. But there's an alternative path forward.
💡 The power shift is real: Local LLMs aren't just about privacy; they're about redistributing AI power away from a handful of tech giants. When individuals, organizations, and even entire nations can run their own models, we're democratizing access to AI capabilities.
🤗 At Hugging Face, we're proud to be at the center of this transformation. Our platform hosts the world's largest library of freely downloadable models, making cutting-edge AI accessible to everyone -- from researchers and developers to curious individuals who want to experiment on their laptops or even smartphones.
The technical barriers that once required $$$ server racks are crumbling. Today, anyone with basic computer skills can download a model, run it locally, and maintain complete control over their AI interactions. No sudden algorithm changes, no data harvesting, no corporate gatekeeping.
This is about technical convenience, but especially about technological sovereignty. When AI power is concentrated in a few hands, we risk creating new forms of digital dependency. Local models offer a path toward genuine AI literacy and independence.
🚀 The future of AI should be open, accessible, and in the hands of the many, not the few. What are your thoughts on AI democratization? Have you experimented with local models yet?
Thrilled to share insights from my recent interview with MIT Technology Review about the growing movement toward local LLMs and what it means for AI democratization. Read here: https://www.technologyreview.com/2025/07/17/1120391/how-to-run-an-llm-on-your-laptop/
🤔 Why this matters: When we use "free" online AI services, we're often the product. Our conversations become training data, our personal stories get "cooked into" models, and our privacy becomes a commodity. But there's an alternative path forward.
💡 The power shift is real: Local LLMs aren't just about privacy; they're about redistributing AI power away from a handful of tech giants. When individuals, organizations, and even entire nations can run their own models, we're democratizing access to AI capabilities.
🤗 At Hugging Face, we're proud to be at the center of this transformation. Our platform hosts the world's largest library of freely downloadable models, making cutting-edge AI accessible to everyone -- from researchers and developers to curious individuals who want to experiment on their laptops or even smartphones.
The technical barriers that once required $$$ server racks are crumbling. Today, anyone with basic computer skills can download a model, run it locally, and maintain complete control over their AI interactions. No sudden algorithm changes, no data harvesting, no corporate gatekeeping.
This is about technical convenience, but especially about technological sovereignty. When AI power is concentrated in a few hands, we risk creating new forms of digital dependency. Local models offer a path toward genuine AI literacy and independence.
🚀 The future of AI should be open, accessible, and in the hands of the many, not the few. What are your thoughts on AI democratization? Have you experimented with local models yet?
Post
276
New blog post alert! "What is the Hugging Face Community Building?", with
@yjernite
and
@irenesolaiman
What 1.8 Million Models Reveal About Open Source Innovation: Our latest deep dive into the Hugging Face Hub reveals patterns that challenge conventional AI narratives:
🔗 Models become platforms for innovation Qwen, Llama, and Gemma models have spawned entire ecosystems of specialized variants. Looking at derivative works shows community adoption better than any single metric.
📊 Datasets reveal the foundation layer → Most downloaded datasets are evaluation benchmarks (MMLU, Squad, GLUE) → Universities and research institutions dominate foundational data → Domain-specific datasets thrive across finance, healthcare, robotics, and science → Open actors provide the datasets that power most AI development
🏛️ Research institutions lead the charge: AI2 (Allen Institute) emerges as one of the most active contributors, alongside significant activity from IBM, NVIDIA, and international organizations. The open source ecosystem spans far beyond Big Tech.
🔍 Interactive exploration tools: We've built several tools to help you discover patterns!
ModelVerse Explorer - organizational contributions
DataVerse Explorer - dataset patterns
Organization HeatMap - activity over time
Base Model Explorer - model family trees
Semantic Search - find models by capability
📚 Academic research is thriving: Researchers are already producing valuable insights, including recent work at FAccT 2025: "The Brief and Wondrous Life of Open Models." We've also made hub datasets, weekly snapshots, and other data available for your own analysis.
The bottom line: AI development is far more distributed, diverse, and collaborative than popular narratives suggest. Real innovation happens through community collaboration across specialized domains.
Read: https://huggingface.co/blog/evijit/hf-hub-ecosystem-overview
What 1.8 Million Models Reveal About Open Source Innovation: Our latest deep dive into the Hugging Face Hub reveals patterns that challenge conventional AI narratives:
🔗 Models become platforms for innovation Qwen, Llama, and Gemma models have spawned entire ecosystems of specialized variants. Looking at derivative works shows community adoption better than any single metric.
📊 Datasets reveal the foundation layer → Most downloaded datasets are evaluation benchmarks (MMLU, Squad, GLUE) → Universities and research institutions dominate foundational data → Domain-specific datasets thrive across finance, healthcare, robotics, and science → Open actors provide the datasets that power most AI development
🏛️ Research institutions lead the charge: AI2 (Allen Institute) emerges as one of the most active contributors, alongside significant activity from IBM, NVIDIA, and international organizations. The open source ecosystem spans far beyond Big Tech.
🔍 Interactive exploration tools: We've built several tools to help you discover patterns!
ModelVerse Explorer - organizational contributions
DataVerse Explorer - dataset patterns
Organization HeatMap - activity over time
Base Model Explorer - model family trees
Semantic Search - find models by capability
📚 Academic research is thriving: Researchers are already producing valuable insights, including recent work at FAccT 2025: "The Brief and Wondrous Life of Open Models." We've also made hub datasets, weekly snapshots, and other data available for your own analysis.
The bottom line: AI development is far more distributed, diverse, and collaborative than popular narratives suggest. Real innovation happens through community collaboration across specialized domains.
Read: https://huggingface.co/blog/evijit/hf-hub-ecosystem-overview
brunatrevelin
authored
a
paper
about 1 month ago
Post
2265
I've been posting bits and pieces about this research, but now I can finally say: new paper alert 🚨
My colleague @brunatrevelin and I just shared a paper exploring why traditional consent frameworks are breaking down in AI contexts (forthcoming chapter in a collective book).
The current model places impossible burdens on users to manage countless consent decisions. Meanwhile, AI systems learn to mimic our voices and writing styles from data we unknowingly provided years ago.
What's next? We need to shift from individual responsibility to collective accountability.
This means:
- Organizations designing systems that respect human agency by default
- Developers building ethics into models from the start
- Policymakers creating frameworks beyond minimal compliance
Blog post: https://huggingface.co/blog/giadap/consentful-ai
Paper: Can AI be Consentful? (2507.01051)
My colleague @brunatrevelin and I just shared a paper exploring why traditional consent frameworks are breaking down in AI contexts (forthcoming chapter in a collective book).
The current model places impossible burdens on users to manage countless consent decisions. Meanwhile, AI systems learn to mimic our voices and writing styles from data we unknowingly provided years ago.
What's next? We need to shift from individual responsibility to collective accountability.
This means:
- Organizations designing systems that respect human agency by default
- Developers building ethics into models from the start
- Policymakers creating frameworks beyond minimal compliance
Blog post: https://huggingface.co/blog/giadap/consentful-ai
Paper: Can AI be Consentful? (2507.01051)
- 2 replies
giadap
authored
a
paper
about 1 month ago
EmilyWitko
posted
an
update
about 1 month ago
Post
1958
Enjoy seven seconds on what I have to say about companies that have hiring quotas and zero other plans to support underrepresented staff:
Post
1913
🗣️ Whose voice do we hear when AI speaks?
Every language carries its own cultural values and worldviews. So, when we build AI systems, we're not just deciding how they speak but also whose perspectives they represent.
Even choosing which dialect to train on in Norway becomes a question of inclusion and power. In Kenya, will AI speak Swahili from Nairobi or coastal regions? What about indigenous languages with rich oral traditions but limited written text, like Quechua in Peru or Cherokee in North America?
The path forward? Building WITH communities, not just FOR them. Working with local partners (libraries, universities, civil society), testing for cultural alignment, and asking hard questions about representation.
Just published some thoughts on this after my keynote in Norway a few weeks ago: https://huggingface.co/blog/giadap/when-ai-speaks
Every language carries its own cultural values and worldviews. So, when we build AI systems, we're not just deciding how they speak but also whose perspectives they represent.
Even choosing which dialect to train on in Norway becomes a question of inclusion and power. In Kenya, will AI speak Swahili from Nairobi or coastal regions? What about indigenous languages with rich oral traditions but limited written text, like Quechua in Peru or Cherokee in North America?
The path forward? Building WITH communities, not just FOR them. Working with local partners (libraries, universities, civil society), testing for cultural alignment, and asking hard questions about representation.
Just published some thoughts on this after my keynote in Norway a few weeks ago: https://huggingface.co/blog/giadap/when-ai-speaks
- 1 reply