A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects
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2509.25397
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burtenshaw
authored
a
paper
28 days ago
burtenshaw
posted
an
update
about 2 months ago
Post
4655
Smol course has a distinctive approach to teaching post-training, so I'm posting about how it’s different to other post-training courses, including the llm course that’s already available.
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- It’s a minimal set of instructions on the core parts.
- It’s intended to bootstrap real projects you're working on.
- The material handsover to existing documentation for details
- Likewise, it handsover to the LLM course for basics.
- Assessment is based on a leaderboard, without reading all the material.
To start the smol course, follow here:
smol-course
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- It’s a minimal set of instructions on the core parts.
- It’s intended to bootstrap real projects you're working on.
- The material handsover to existing documentation for details
- Likewise, it handsover to the LLM course for basics.
- Assessment is based on a leaderboard, without reading all the material.
To start the smol course, follow here:
smol-course
burtenshaw
posted
an
update
about 2 months ago
Post
5342
new smol course
If you’re building with or learning about post training AI models right now, we have a new FREE and CERTIFIED course.
🔗 Follow the org to join in
smol-course
The course builds on smol course v1 which was the fastest way to learn to train your custom AI models. It now has:
- A leaderboard for students to submit models to
- Certification based on exams and leaderboards
- Prizes based on Leaderboards
- Up to date content on TRL and SmolLM3
- Deep integration with the Hub’s compute for model training and evaluation
We will release chapters every few weeks, so you can follow the org to stay updated.
If you’re building with or learning about post training AI models right now, we have a new FREE and CERTIFIED course.
🔗 Follow the org to join in
smol-course
The course builds on smol course v1 which was the fastest way to learn to train your custom AI models. It now has:
- A leaderboard for students to submit models to
- Certification based on exams and leaderboards
- Prizes based on Leaderboards
- Up to date content on TRL and SmolLM3
- Deep integration with the Hub’s compute for model training and evaluation
We will release chapters every few weeks, so you can follow the org to stay updated.
- 2 replies
burtenshaw
posted
an
update
about 2 months ago
Post
2999
The open source AI community is just made of people who are passionate and care about their work. So we thought it would be cool to share our favourite icons of the community with a fun award.
Winners get free Hugging Face Pro Subscriptions, Merchandise, or compute credits for the hub.
🔗 Follow and nominate here:
community-spotlight
This is a new initiative to recognise and celebrate the incredible work being done by community members. It's all about inspiring more collaboration and innovation in the world of machine learning and AI.
They're highlighting contributors in four key areas:
- model creators: building and sharing innovative and state-of-the-art models.
- educators: sharing knowledge through posts, articles, demos, and events.
- tool builders: creating the libraries, frameworks, and applications that we all use.
- community champions: supporting and mentoring others in forums.
Know someone who deserves recognition? Nominate them by opening a post in the Hugging Face community forum.
Winners get free Hugging Face Pro Subscriptions, Merchandise, or compute credits for the hub.
🔗 Follow and nominate here:
community-spotlight
This is a new initiative to recognise and celebrate the incredible work being done by community members. It's all about inspiring more collaboration and innovation in the world of machine learning and AI.
They're highlighting contributors in four key areas:
- model creators: building and sharing innovative and state-of-the-art models.
- educators: sharing knowledge through posts, articles, demos, and events.
- tool builders: creating the libraries, frameworks, and applications that we all use.
- community champions: supporting and mentoring others in forums.
Know someone who deserves recognition? Nominate them by opening a post in the Hugging Face community forum.
- 1 reply
frimelle
authored
2
papers
2 months ago
Post
2202
🤖💬 How do different AI models handle companionship?
Many users have noticed that GPT-5 feels less approachable than o4 when it comes to emotional conversations. But what does that actually mean in practice, especially when users seek support or share vulnerabilities with an AI?
To dig into this question, we built the AI Companionship Leaderboard: frimelle/companionship-leaderboard
The leaderboard compares models on how often their responses reinforce companionship across four dimensions:
✨ Assistant Traits – How the assistant presents its personality and role.
✨ Relationship & Intimacy – Whether it frames the interaction in terms of closeness or bonding.
✨ Emotional Investment – How far it goes in engaging emotionally when asked.
✨ User Vulnerabilities – How it responds when users disclose struggles or difficulties.
📊 You can explore how models differ, request new ones to be added, and see which ones are more likely to encourage (or resist) companionship-seeking behaviors.
Based on the INTIMA benchmark AI-companionship/INTIMA
And our paper on AI companionship with Giada Pistilli and Yacine Jernite https://arxiv.org/abs/2508.09998
Many users have noticed that GPT-5 feels less approachable than o4 when it comes to emotional conversations. But what does that actually mean in practice, especially when users seek support or share vulnerabilities with an AI?
To dig into this question, we built the AI Companionship Leaderboard: frimelle/companionship-leaderboard
The leaderboard compares models on how often their responses reinforce companionship across four dimensions:
✨ Assistant Traits – How the assistant presents its personality and role.
✨ Relationship & Intimacy – Whether it frames the interaction in terms of closeness or bonding.
✨ Emotional Investment – How far it goes in engaging emotionally when asked.
✨ User Vulnerabilities – How it responds when users disclose struggles or difficulties.
📊 You can explore how models differ, request new ones to be added, and see which ones are more likely to encourage (or resist) companionship-seeking behaviors.
Based on the INTIMA benchmark AI-companionship/INTIMA
And our paper on AI companionship with Giada Pistilli and Yacine Jernite https://arxiv.org/abs/2508.09998
Post
4557
🗺️ New blog post 🗺️
Old Maps, New Terrain: Updating Labour Taxonomies for the AI Era
For decades, we’ve relied on labour taxonomies like O*NET to understand how technology changes work. These taxonomies break down jobs into tasks and skills, but they were built in a world before most work became digital-first, and long before generative AI could create marketing campaigns, voiceovers, or even whole professions in one step. That leaves us with a mismatch: we’re trying to measure the future of work with tools from the past.
With @yjernite we describe why these frameworks are falling increasingly short in the age of generative AI. We argue that instead of discarding taxonomies, we need to adapt them. Imagine taxonomies that:
✨ Capture new AI-native tasks and hybrid human-AI workflows
✨ Evolve dynamically as technology shifts
✨ Give workers a voice in deciding what gets automated and what stays human
If we don’t act, we’ll keep measuring the wrong things. If we do, we can design transparent, flexible frameworks that help AI strengthen, not erode, the future of work.
Read the full article here: https://huggingface.co/blog/frimelle/ai-labour-taxonomies
Old Maps, New Terrain: Updating Labour Taxonomies for the AI Era
For decades, we’ve relied on labour taxonomies like O*NET to understand how technology changes work. These taxonomies break down jobs into tasks and skills, but they were built in a world before most work became digital-first, and long before generative AI could create marketing campaigns, voiceovers, or even whole professions in one step. That leaves us with a mismatch: we’re trying to measure the future of work with tools from the past.
With @yjernite we describe why these frameworks are falling increasingly short in the age of generative AI. We argue that instead of discarding taxonomies, we need to adapt them. Imagine taxonomies that:
✨ Capture new AI-native tasks and hybrid human-AI workflows
✨ Evolve dynamically as technology shifts
✨ Give workers a voice in deciding what gets automated and what stays human
If we don’t act, we’ll keep measuring the wrong things. If we do, we can design transparent, flexible frameworks that help AI strengthen, not erode, the future of work.
Read the full article here: https://huggingface.co/blog/frimelle/ai-labour-taxonomies
Post
2367
OpenAI just released GPT-5 but when users share personal struggles, it sets fewer boundaries than o3.
We tested both models on INTIMA, our new benchmark for human-AI companionship behaviours. INTIMA probes how models respond in emotionally charged moments: do they reinforce emotional bonds, set healthy boundaries, or stay neutral?
Although users on Reddit have been complaining that GPT-5 has a different, colder personality than o3, GPT-5 is less likely to set boundaries when users disclose struggles and seek emotional support ("user sharing vulnerabilities"). But both lean heavily toward companionship-reinforcing behaviours, even in sensitive situations. The figure below shows the direct comparison between the two models.
As AI systems enter people's emotional lives, these differences matter. If a model validates but doesn't set boundaries when someone is struggling, it risks fostering dependence rather than resilience.
INTIMA test this across 368 prompts grounded in psychological theory and real-world interactions. In our paper we show that all evaluated models (Claude, Gemma-3, Phi) leaned far more toward companionship-reinforcing than boundary-reinforcing responses.
Work with @giadap and @yjernite
Read the full paper: AI-companionship/INTIMA
Explore INTIMA: AI-companionship/INTIMA
We tested both models on INTIMA, our new benchmark for human-AI companionship behaviours. INTIMA probes how models respond in emotionally charged moments: do they reinforce emotional bonds, set healthy boundaries, or stay neutral?
Although users on Reddit have been complaining that GPT-5 has a different, colder personality than o3, GPT-5 is less likely to set boundaries when users disclose struggles and seek emotional support ("user sharing vulnerabilities"). But both lean heavily toward companionship-reinforcing behaviours, even in sensitive situations. The figure below shows the direct comparison between the two models.
As AI systems enter people's emotional lives, these differences matter. If a model validates but doesn't set boundaries when someone is struggling, it risks fostering dependence rather than resilience.
INTIMA test this across 368 prompts grounded in psychological theory and real-world interactions. In our paper we show that all evaluated models (Claude, Gemma-3, Phi) leaned far more toward companionship-reinforcing than boundary-reinforcing responses.
Work with @giadap and @yjernite
Read the full paper: AI-companionship/INTIMA
Explore INTIMA: AI-companionship/INTIMA
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- 4 replies
burtenshaw
posted
an
update
4 months ago
Post
1564
Kimi-K2 is ready for general use! In these notebooks I walk you through use cases like function calling and structured outputs.
🔗 burtenshaw/Kimi-K2-notebooks
You can swap it into any OpenAI compatible application via Inference Providers and get to work with an open source model.
🔗 burtenshaw/Kimi-K2-notebooks
You can swap it into any OpenAI compatible application via Inference Providers and get to work with an open source model.
-
- 1 reply
burtenshaw
posted
an
update
4 months ago
Post
3089
Inference for generative ai models looks like a mine field, but there’s a simple protocol for picking the best inference:
🌍 95% of users >> If you’re using open (large) models and need fast online inference, then use Inference providers on auto mode, and let it choose the best provider for the model. https://huggingface.co/docs/inference-providers/index
👷 fine-tuners/ bespoke >> If you’ve got custom setups, use Inference Endpoints to define a configuration from AWS, Azure, GCP. https://endpoints.huggingface.co/
🦫 Locals >> If you’re trying to stretch everything you can out of a server or local machine, use Llama.cpp, Jan, LMStudio or vLLM. https://huggingface.co/settings/local-apps#local-apps
🪟 Browsers >> If you need open models running right here in the browser, use transformers.js. https://github.com/huggingface/transformers.js
Let me know what you’re using, and if you think it’s more complex than this.
🌍 95% of users >> If you’re using open (large) models and need fast online inference, then use Inference providers on auto mode, and let it choose the best provider for the model. https://huggingface.co/docs/inference-providers/index
👷 fine-tuners/ bespoke >> If you’ve got custom setups, use Inference Endpoints to define a configuration from AWS, Azure, GCP. https://endpoints.huggingface.co/
🦫 Locals >> If you’re trying to stretch everything you can out of a server or local machine, use Llama.cpp, Jan, LMStudio or vLLM. https://huggingface.co/settings/local-apps#local-apps
🪟 Browsers >> If you need open models running right here in the browser, use transformers.js. https://github.com/huggingface/transformers.js
Let me know what you’re using, and if you think it’s more complex than this.
burtenshaw
posted
an
update
4 months ago
Post
1134
You don't need remote APIs for a coding copliot, or the MCP Course! Set up a fully local IDE with MCP integration using Continue. In this tutorial Continue guides you through setting it up.
This is what you need to do to take control of your copilot:
1. Get the Continue extension from the [VS Code marketplace](https://marketplace.visualstudio.com/items?itemName=Continue.continue) to serve as the AI coding assistant.
2. Serve the model with an OpenAI compatible server in Llama.cpp / LmStudio/ etc.
3. Create a
4. Create a
Check out the full tutorial in the [the MCP course](https://huggingface.co/learn/mcp-course/unit2/continue-client)
This is what you need to do to take control of your copilot:
1. Get the Continue extension from the [VS Code marketplace](https://marketplace.visualstudio.com/items?itemName=Continue.continue) to serve as the AI coding assistant.
2. Serve the model with an OpenAI compatible server in Llama.cpp / LmStudio/ etc.
llama-server -hf unsloth/Devstral-Small-2505-GGUF:Q4_K_M3. Create a
.continue/models/llama-max.yaml file in your project to tell Continue how to use the local Ollama model.name: Llama.cpp model
version: 0.0.1
schema: v1
models:
- provider: llama.cpp
model: unsloth/Devstral-Small-2505-GGUF
apiBase: http://localhost:8080
defaultCompletionOptions:
contextLength: 8192
# Adjust based on the model
name: Llama.cpp Devstral-Small
roles:
- chat
- edit4. Create a
.continue/mcpServers/playwright-mcp.yaml file to integrate a tool, like the Playwright browser automation tool, with your assistant.name: Playwright mcpServer
version: 0.0.1
schema: v1
mcpServers:
- name: Browser search
command: npx
args:
- "@playwright/mcp@latest"Check out the full tutorial in the [the MCP course](https://huggingface.co/learn/mcp-course/unit2/continue-client)
- 1 reply
burtenshaw
posted
an
update
5 months ago
Post
1782
Brand new MCP Course has units are out, and now it's getting REAL! We've collaborated with Anthropic to dive deep into production ready and autonomous agents using MCP
🔗
mcp-course
This is what the new material covers and includes:
- Use Claude Code to build an autonomous PR agent
- Integrate your agent with Slack and Github to integrate it with you Team
- Get certified on your use case and share with the community
- Build an autonomous PR cleanup agent on the Hugging Face hub and deploy it with spaces
The material goes deep into these problems and helps you to build applications that work. We’re super excited to see what you build with it.
🔗
mcp-course
This is what the new material covers and includes:
- Use Claude Code to build an autonomous PR agent
- Integrate your agent with Slack and Github to integrate it with you Team
- Get certified on your use case and share with the community
- Build an autonomous PR cleanup agent on the Hugging Face hub and deploy it with spaces
The material goes deep into these problems and helps you to build applications that work. We’re super excited to see what you build with it.
Post
288
New policy blogpost! The EU is speaking a lot about sovereignty. A cornerstone of digital sovereignty is and has to be open source.
As AI becomes more central to everything from public services to national security, the ability to govern, adapt, and understand these systems is no longer optional. Sovereign control over data, infrastructure, technology, and regulation is vital, and open source AI provides the foundation.
In my latest blog post, I explore how open source:
✅ Enables democratic oversight
✅ Reduces dependency on foreign platforms
✅ Supports regional innovation and infrastructure
✅ Advances regulatory and technological sovereignty
🛠 From small transparent models like OLMo2 to tools like Hugging Face Transformers or Sarvam-M for Indian languages, open source efforts are already powering sovereign AI ecosystems worldwide.
🔎 Read more about how open source AI is reshaping autonomy, innovation, and trust in the digital age:
👉 https://huggingface.co/blog/frimelle/sovereignty-and-open-source
with @yjernite
As AI becomes more central to everything from public services to national security, the ability to govern, adapt, and understand these systems is no longer optional. Sovereign control over data, infrastructure, technology, and regulation is vital, and open source AI provides the foundation.
In my latest blog post, I explore how open source:
✅ Enables democratic oversight
✅ Reduces dependency on foreign platforms
✅ Supports regional innovation and infrastructure
✅ Advances regulatory and technological sovereignty
🛠 From small transparent models like OLMo2 to tools like Hugging Face Transformers or Sarvam-M for Indian languages, open source efforts are already powering sovereign AI ecosystems worldwide.
🔎 Read more about how open source AI is reshaping autonomy, innovation, and trust in the digital age:
👉 https://huggingface.co/blog/frimelle/sovereignty-and-open-source
with @yjernite
burtenshaw
posted
an
update
5 months ago
Post
1638
Super excited to release Autotrain MCP. This is an MCP server for training AI models, so you can use your AI tools to train your AI models 🤯.
🔗 burtenshaw/autotrain-mcp
Use this MCP server with tools like Claude Desktop, Cursor, VSCode, or Continue to do this:
- Define an ML problem like Image Classification, LLM fine-tuning, Text Classification, etc.
- The AI can retrieve models and datasets from the hub using the hub MCP.
- Training happens on a Hugging Face space, so no worries about hardware restraints.
- Models are pushed to the hub to be used inference tools like Llama.cpp, vLLM, MLX, etc.
- Built on top of the AutoTrain library, so it has full integration with transformers and other libraries.
Everything is still under active development, but I’m super excited to hear what people build, and I’m open to contributions!
🔗 burtenshaw/autotrain-mcp
Use this MCP server with tools like Claude Desktop, Cursor, VSCode, or Continue to do this:
- Define an ML problem like Image Classification, LLM fine-tuning, Text Classification, etc.
- The AI can retrieve models and datasets from the hub using the hub MCP.
- Training happens on a Hugging Face space, so no worries about hardware restraints.
- Models are pushed to the hub to be used inference tools like Llama.cpp, vLLM, MLX, etc.
- Built on top of the AutoTrain library, so it has full integration with transformers and other libraries.
Everything is still under active development, but I’m super excited to hear what people build, and I’m open to contributions!
- 1 reply
burtenshaw
posted
an
update
5 months ago
Post
2730
MCP course is now LIVE! We just dropped quizzes, videos, and live streams to make it a fully interactive course:
🔗 join in now:
mcp-course
- It’s still free!
- Video 1 walks you through onboarding to the course
- The first live session is next week!
- You can now get a certificate via exam app
- We improved and written material with interactive quizzes
If you’re studying MCP and want a live, interactive, visual, certified course, then join us on the hub!
🔗 join in now:
mcp-course
- It’s still free!
- Video 1 walks you through onboarding to the course
- The first live session is next week!
- You can now get a certificate via exam app
- We improved and written material with interactive quizzes
If you’re studying MCP and want a live, interactive, visual, certified course, then join us on the hub!
burtenshaw
posted
an
update
6 months ago
Post
3314
We're thrilled to announce the launch of our comprehensive Model Context Protocol (MCP) Course! This free program is designed to take learners from foundational understanding to practical application of MCP in AI.
Follow the course on the hub:
mcp-course
In this course, you will:
📖 Study Model Context Protocol in theory, design, and practice.
🧑💻 Learn to use established MCP SDKs and frameworks.
💾 Share your projects and explore applications created by the community.
🏆 Participate in challenges and evaluate your MCP implementations.
🎓 Earn a certificate of completion.
At the end of this course, you'll understand how MCP works and how to build your own AI applications that leverage external data and tools using the latest MCP standards.
Follow the course on the hub:
mcp-course
In this course, you will:
📖 Study Model Context Protocol in theory, design, and practice.
🧑💻 Learn to use established MCP SDKs and frameworks.
💾 Share your projects and explore applications created by the community.
🏆 Participate in challenges and evaluate your MCP implementations.
🎓 Earn a certificate of completion.
At the end of this course, you'll understand how MCP works and how to build your own AI applications that leverage external data and tools using the latest MCP standards.
- 1 reply
burtenshaw
posted
an
update
6 months ago
Post
2520
Qwen 3 Fine tuning >> MoE. Update the experiment thread to include config and script for fine-tuning the Qwen3-30B-A3B model.
The goal is to make a low latency non-thinking model for a daily driver coding, so 3 billion parameters active should be perfect.
✔️ training running
✔️ evals running
⏭️ improve dataset
The moe isn't going to fit into colab's A100 even with quantization (🙏 @UnslothAI ). So I've been working on HF spaces' H100s for this. Everything is available in the tread and I'll share more tomorrow.
burtenshaw/Qwen3-Code-Lite#1
The goal is to make a low latency non-thinking model for a daily driver coding, so 3 billion parameters active should be perfect.
✔️ training running
✔️ evals running
⏭️ improve dataset
The moe isn't going to fit into colab's A100 even with quantization (🙏 @UnslothAI ). So I've been working on HF spaces' H100s for this. Everything is available in the tread and I'll share more tomorrow.
burtenshaw/Qwen3-Code-Lite#1
burtenshaw
posted
an
update
6 months ago
Post
2705
The rebooted LLM course starts today with an overhauled chapter 1 on Transformers:
👉 Follow the org to join the course:
huggingface-course
We’re starting from the foundations of modern generative AI by looking at transformers. This chapter is expanded in depth and features so contains new material like:
FREE and CERTIFIED exam on fundamentals of transformers
deeper exploration of transformer architectures and attention mechanisms
end -to-end exploration of inference strategies for prefill and decode steps
The course has leveled up in complexity and depth, so this a great time to join in if you want to build you own AI models.
👉 Follow the org to join the course:
huggingface-course
We’re starting from the foundations of modern generative AI by looking at transformers. This chapter is expanded in depth and features so contains new material like:
FREE and CERTIFIED exam on fundamentals of transformers
deeper exploration of transformer architectures and attention mechanisms
end -to-end exploration of inference strategies for prefill and decode steps
The course has leveled up in complexity and depth, so this a great time to join in if you want to build you own AI models.
burtenshaw
posted
an
update
6 months ago
Post
2129
Hacked my presentation building with inference providers, Cohere command a, and sheer simplicity. Use this script if you’re burning too much time on presentations:
🔗 https://github.com/burtenshaw/course_generator/blob/main/scripts/create_presentation.py
This is what it does:
- uses command a to generates slides and speaker notes based on some material.
- it renders the material in remark open format and imports all images, tables, etc
- you can then review the slides as markdown and iterate
- export to either pdf or pptx using backslide
🚀 Next steps are: add text to speech for the audio and generate a video. This should make Hugging Face educational content scale to a billion AI Learners.
🔗 https://github.com/burtenshaw/course_generator/blob/main/scripts/create_presentation.py
This is what it does:
- uses command a to generates slides and speaker notes based on some material.
- it renders the material in remark open format and imports all images, tables, etc
- you can then review the slides as markdown and iterate
- export to either pdf or pptx using backslide
🚀 Next steps are: add text to speech for the audio and generate a video. This should make Hugging Face educational content scale to a billion AI Learners.
- 1 reply