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davanstrienΒ
updated
a
dataset
about 5 hours ago
burtenshawΒ
authored
a
paper
4 months ago
burtenshawΒ
posted
an
update
5 months ago
Post
6430
Smol course has a distinctive approach to teaching post-training, so I'm posting about how itβs different to other post-training courses, including the llm course thatβs already available.
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- Itβs a minimal set of instructions on the core parts.
- Itβs 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.
- Itβs a minimal set of instructions on the core parts.
- Itβs 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:
burtenshawΒ
posted
an
update
5 months ago
Post
5444
new smol course
If youβre building with or learning about post training AI models right now, we have a new FREE and CERTIFIED course.
π 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 Hubβs compute for model training and evaluation
We will release chapters every few weeks, so you can follow the org to stay updated.
If youβre building with or learning about post training AI models right now, we have a new FREE and CERTIFIED course.
π Follow the org to join in
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 Hubβs compute for model training and evaluation
We will release chapters every few weeks, so you can follow the org to stay updated.
- 2 replies
burtenshawΒ
posted
an
update
5 months ago
Post
3126
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.
π 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.
π Follow and nominate here:
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
davanstrienΒ
posted
an
update
5 months ago
Post
1690
I fine-tuned a smol VLM to generate specialized art history metadata!
https://huggingface.co/davanstrien/iconclass-vlm: Qwen2.5-VL-3B trained using SFT to generate ICONCLASS codes (think Dewey Decimal for art!)
Trained with TRL + HF Jobs - single UV script, no GPU needed!
Space to explore predictions on a test set: davanstrien/iconclass-predictions
Blog soon!
https://huggingface.co/davanstrien/iconclass-vlm: Qwen2.5-VL-3B trained using SFT to generate ICONCLASS codes (think Dewey Decimal for art!)
Trained with TRL + HF Jobs - single UV script, no GPU needed!
Space to explore predictions on a test set: davanstrien/iconclass-predictions
Blog soon!
- 1 reply
burtenshawΒ
posted
an
update
6 months ago
Post
1596
Kimi-K2 is ready for general use! In these notebooks I walk you through use cases like function calling and structured outputs.
π 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.
π 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
burtenshawΒ
posted
an
update
7 months ago
Post
3121
Inference for generative ai models looks like a mine field, but thereβs a simple protocol for picking the best inference:
π 95% of users >> If youβre 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
π· fine-tuners/ bespoke >> If youβve got custom setups, use Inference Endpoints to define a configuration from AWS, Azure, GCP. https://endpoints.huggingface.co/
𦫠Locals >> If youβre 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
πͺ 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 youβre using, and if you think itβs more complex than this.
π 95% of users >> If youβre 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
π· fine-tuners/ bespoke >> If youβve got custom setups, use Inference Endpoints to define a configuration from AWS, Azure, GCP. https://endpoints.huggingface.co/
𦫠Locals >> If youβre 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
πͺ 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 youβre using, and if you think itβs more complex than this.
frascuchonΒ
posted
an
update
7 months ago
Post
2741
Extended Dataset with Sheets π
I used Sheets to extend the fka/awesome-chatgpt-prompts dataset with a single prompt π‘. Check out the result: https://huggingface.co/datasets/frascuchon/extended_fka_awesome_chatgpt_prompts
Try Sheets to expand your datasets: aisheets/sheets π οΈ
I used Sheets to extend the fka/awesome-chatgpt-prompts dataset with a single prompt π‘. Check out the result: https://huggingface.co/datasets/frascuchon/extended_fka_awesome_chatgpt_prompts
Try Sheets to expand your datasets: aisheets/sheets π οΈ
frascuchonΒ
posted
an
update
7 months ago
Post
825
π Are you ready to take control of your data? π Follow the step-by-step guide to setup and run Sheets locally on your own machine π₯οΈ!
π» Click the link to get started and become a Sheets master π―!
π https://huggingface.co/blog/frascuchon/running-sheets-locally
Try Sheet π
aisheets
π» Click the link to get started and become a Sheets master π―!
π https://huggingface.co/blog/frascuchon/running-sheets-locally
Try Sheet π
frascuchonΒ
posted
an
update
7 months ago
Post
2879
Extending datasets just got a whole lot easier! π With Sheets, I was able to create a Spanish version of the popular fka/awesome-chatgpt-prompts dataset in just a few minutes β±οΈ.
Check out the resulting dataset: frascuchon/fka_awesome_chatgpt_es π
Want to try it out for yourself? Head over to the Sheets space and see how easy it is to extend and modify existing datasets π€―. The possibilities are endless! π
Check out the resulting dataset: frascuchon/fka_awesome_chatgpt_es π
Want to try it out for yourself? Head over to the Sheets space and see how easy it is to extend and modify existing datasets π€―. The possibilities are endless! π
burtenshawΒ
posted
an
update
7 months ago
Post
1154
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
burtenshawΒ
posted
an
update
8 months ago
Post
1795
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
π
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. Weβre super excited to see what you build with it.
π
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. Weβre super excited to see what you build with it.
burtenshawΒ
posted
an
update
8 months ago
Post
1660
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 π€―.
π 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 Iβm super excited to hear what people build, and Iβm open to contributions!
π 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 Iβm super excited to hear what people build, and Iβm open to contributions!
- 1 reply
frascuchonΒ
posted
an
update
8 months ago
Post
1345
Unlock the full potential of your datasets with SHEETS! It's incredibly easy to extend existing datasets and unlock new insights.
Leverage open-source models to translate, summarize, classify, and more - all directly within your existing columns.
Ready to give it a try? Explore the possibilities here: aisheets/sheets
Leverage open-source models to translate, summarize, classify, and more - all directly within your existing columns.
Ready to give it a try? Explore the possibilities here: aisheets/sheets
-
- 2 replies
davanstrienΒ
posted
an
update
8 months ago
Post
3692
Inspired by Hugging Face's official MCP server, I've developed a complementary tool that exposes my semantic search API to enhance discovery across the HF platform.
Key capabilities:
- AI-powered semantic search for models and datasets
- Parameter count analysis via safetensors metadata
- Trending content discovery
- Find similar models/datasets functionality
- 11 tools total for enhanced ecosystem navigation
The semantic search goes beyond simple keyword matching, understanding context and relationships between different models and datasets.
Example query: "Find around 10 reasoning Hugging Face datasets published in 2025 focusing on topics other than maths and science. Show a link and a short summary for each dataset." (results in video!)
https://github.com/davanstrien/hub-semantic-search-mcp
Key capabilities:
- AI-powered semantic search for models and datasets
- Parameter count analysis via safetensors metadata
- Trending content discovery
- Find similar models/datasets functionality
- 11 tools total for enhanced ecosystem navigation
The semantic search goes beyond simple keyword matching, understanding context and relationships between different models and datasets.
Example query: "Find around 10 reasoning Hugging Face datasets published in 2025 focusing on topics other than maths and science. Show a link and a short summary for each dataset." (results in video!)
https://github.com/davanstrien/hub-semantic-search-mcp
- 1 reply
frascuchonΒ
posted
an
update
8 months ago
Post
3011
Hey! I built RAG MCP Server Space, a simple Gradio MCP server for RAG systems that allows you to search relevant results without passing huge contexts to your LLM.
You can use this space to integrate with your agents and improve the efficiency of your search results. Feel free to try it out and let me know if you have any feedback or questions!
frascuchon/rag-mcp-server
Thanks for checking it out!
You can use this space to integrate with your agents and improve the efficiency of your search results. Feel free to try it out and let me know if you have any feedback or questions!
frascuchon/rag-mcp-server
Thanks for checking it out!
burtenshawΒ
posted
an
update
8 months ago
Post
2741
MCP course is now LIVE! We just dropped quizzes, videos, and live streams to make it a fully interactive course:
π join in now:
mcp-course
- Itβs 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 youβre studying MCP and want a live, interactive, visual, certified course, then join us on the hub!
π join in now:
- Itβs 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 youβre studying MCP and want a live, interactive, visual, certified course, then join us on the hub!
burtenshawΒ
posted
an
update
9 months ago
Post
3324
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:
π Study Model Context Protocol in theory, design, and practice.
π§βπ» Learn to use established MCP SDKs and frameworks.
πΎ Share your projects and explore applications created by the community.
π Participate in challenges and evaluate your MCP implementations.
π 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:
In this course, you will:
π Study Model Context Protocol in theory, design, and practice.
π§βπ» Learn to use established MCP SDKs and frameworks.
πΎ Share your projects and explore applications created by the community.
π Participate in challenges and evaluate your MCP implementations.
π 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
burtenshawΒ
posted
an
update
9 months ago
Post
2555
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.
βοΈ training running
βοΈ evals running
βοΈ improve dataset
The moe isn't going to fit into colab's A100 even with quantization (π @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.
βοΈ training running
βοΈ evals running
βοΈ improve dataset
The moe isn't going to fit into colab's A100 even with quantization (π @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