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AI & ML interests

A central place for all models and datasets created in the HuggingFace course.

Recent Activity

lvwerra  authored a paper 9 days ago
FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language
burtenshaw  updated a Space 18 days ago
huggingface-course/README
burtenshaw  published a Space 18 days ago
huggingface-course/unit_3_quiz
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sergiopaniego 
posted an update 3 days ago
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1623
Updated my HF Space for vibe testing smol VLMs on object detection, visual grounding, keypoint detection & counting! 👓

🆕 Compare Qwen2.5 VL 3B vs Moondream 2B side-by-side with annotated images & text outputs.

Try examples or test your own images! 🏃

📱Space: sergiopaniego/vlm_object_understanding
burtenshaw 
posted an update 5 days ago
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2455
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.
sergiopaniego 
posted an update 6 days ago
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964
📣 CALL FOR CONTRIBUTORS! 📣

Following last week’s full release of Gemma 3n, we launched a dedicated recipes repo to explore and share use cases. We already added some! 🧑‍🍳

Now we’re inviting the community to contribute and showcase how these models shine! ✨

Let them cook.

Check it out: https://github.com/huggingface/huggingface-gemma-recipes/issues/4
  • 1 reply
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lvwerra 
authored a paper 9 days ago
sergiopaniego 
posted an update 12 days ago
burtenshaw 
updated a Space 18 days ago
burtenshaw 
published a Space 18 days ago
burtenshaw 
updated a Space 19 days ago
burtenshaw 
published a dataset 19 days ago
burtenshaw 
posted an update 19 days ago
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910
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
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burtenshaw 
updated a dataset 23 days ago
reach-vb 
posted an update 23 days ago
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2519
Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub 🤯

Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥

Go, play with it today: https://huggingface.co/blog/inference-providers-featherless

P.S. They're also bringing on more GPUs to support all your concurrent requests!
burtenshaw 
posted an update 23 days ago
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1596
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.
julien-c 
in huggingface-course/chapter_1_exam 24 days ago

Fundamentals of LLM

1
#42 opened about 1 month ago by
shrutisattigeri
burtenshaw 
posted an update 24 days ago
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1498
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
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Steveeeeeeen 
updated a Space about 1 month ago
julien-c 
in huggingface-course/chapter_1_exam about 1 month ago

Certificate of Achievement: 1. Fundamentals of LLMs

1
#36 opened about 1 month ago by
vincenzodevivo

YES :)

1
#34 opened about 1 month ago by
NikoBOBA
burtenshaw 
posted an update about 1 month ago
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2632
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!
reach-vb 
posted an update about 2 months ago
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4038
hey hey @mradermacher - VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! 💥

as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!

in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.

p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage

p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
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