Hugging Face's logo

a smol course
university
https://github.com/huggingface/smol-course
Activity Feed

AI & ML interests

The smollest course on post training

Recent Activity

burtenshaw  updated a Space 1 day ago
smol-course/leaderboard
burtenshaw  updated a Space 2 days ago
smol-course/README
burtenshaw  published a dataset 2 days ago
smol-course/details_HuggingFaceTB__SmolLM3-3B_private
View all activity

burtenshaw 
posted an update 1 day ago
view post
Post
696
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.
  • 1 reply
·
burtenshaw 
updated a Space 1 day ago
burtenshaw 
updated a Space 2 days ago
burtenshaw 
published a dataset 2 days ago
burtenshaw 
updated a dataset 2 days ago
burtenshaw 
published a Space 3 days ago
burtenshaw 
updated a dataset 4 days ago
burtenshaw 
published a dataset 4 days ago
burtenshaw 
published a Space 4 days ago
burtenshaw 
posted an update about 2 months ago
view post
Post
1377
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.
  • 1 reply
·
burtenshaw 
posted an update 2 months ago
view post
Post
2929
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.
burtenshaw 
posted an update 3 months ago
view post
Post
1019
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.

llama-server -hf unsloth/Devstral-Small-2505-GGUF:Q4_K_M

3. 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
          - edit


4. 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 3 months ago
view post
Post
1684
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.
burtenshaw 
posted an update 3 months ago
view post
Post
1565
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!
  • 1 reply
·
burtenshaw 
posted an update 4 months ago
view post
Post
2674
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!
burtenshaw 
posted an update 4 months ago
view post
Post
3267
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.
  • 1 reply
·
burtenshaw 
posted an update 4 months ago
view post
Post
2447
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
burtenshaw 
posted an update 5 months ago
view post
Post
2675
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.
burtenshaw 
posted an update 5 months ago
view post
Post
2109
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.
  • 1 reply
·
burtenshaw 
posted an update 5 months ago
view post
Post
3411
NEW UNIT in the Hugging Face Reasoning course. We dive deep into the algorithm behind DeepSeek R1 with an advanced and hands-on guide to interpreting GRPO.

🔗 reasoning-course

This unit is super useful if you’re tuning models with reinforcement learning. It will help with:

- interpreting loss and reward progression during training runs
- selecting effective parameters for training
- reviewing and defining effective reward functions

This unit also works up smoothly toward the existing practical exercises form @mlabonne and Unsloth.

📣 Shout out to @ShirinYamani who wrote the unit. Follow for more great content.
  • 1 reply
·