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burtenshaw

posted an update about 12 hours ago

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99

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.

Jenbenarye

updated a dataset 1 day ago

feel-fl/feel-feedback

Viewer • Updated 1 day ago • 1.22k • 1.52k • 2

burtenshaw

posted an update 1 day ago

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641

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

posted an update 21 days ago

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2521

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 28 days ago

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3153

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

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burtenshaw

posted an update about 1 month ago

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2233

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

riddhib

updated a Space about 2 months ago

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🚀

Generate conversational responses in multiple languages

burtenshaw

posted an update about 2 months ago

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2609

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.

Jenbenarye

updated a Space about 2 months ago

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🚀

Generate conversational responses in multiple languages

burtenshaw

posted an update about 2 months ago

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2061

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

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borgr

authored a paper 2 months ago

Pretraining Language Models for Diachronic Linguistic Change Discovery

Paper • 2504.05523 • Published Apr 7 • 6

burtenshaw

posted an update 3 months ago

Post

3323

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

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burtenshaw

posted an update 3 months ago

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3900

The Hugging Face Agents Course now includes three major agent frameworks!

🔗

agents-course

This includes LlamaIndex, LangChain, and our very own smolagents. We've worked to integrate the three frameworks in distinctive ways so that learners can reflect on when and where to use each.

This also means that you can follow the course if you're already familiar with one of these frameworks, and soak up some of the fundamental knowledge in earlier units.

Hopefully, this makes the agents course as open to as many people as possible.

3 replies

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burtenshaw

posted an update 3 months ago

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2576

The open LLM leaderboard is completed, retired, dead, ‘ascended to a higher plane’. And in its shadow we have an amazing range of leaderboards built and maintained by the community.

In this post, I just want to list some of those great leaderboards that you should bookmark for staying up to date:

- Chatbot Arena LLM Leaderboard is the first port of call for checking out the best model. It’s not the fastest because humans will need to use the models to get scores, but it’s worth the wait. lmarena-ai/chatbot-arena-leaderboard

- OpenVLM Leaderboard is great for getting scores on vision language models opencompass/open_vlm_leaderboard

- Ai2 are doing a great job on RewardBench and I hope they keep it up because reward models are the unsexy workhorse of the field. allenai/reward-bench

- The GAIA leaderboard is great for evaluating agent applications. gaia-benchmark/leaderboard

🤩 This seems like such a sustainable way of building for the long term, where rather than leaning on a single company to evaluate all LLMs, we share the load.

3 replies

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burtenshaw

posted an update 3 months ago

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2281

Still speed running Gemma 3 to think. Today I focused on setting up gpu poor hardware to run GRPO.

This is a plain TRL and PEFT notebook which works on mac silicone or colab T4. This uses the 1b variant of Gemma 3 and a reasoning version of GSM8K dataset.

🧑‍🍳 There’s more still in the oven like releasing models, an Unsloth version, and deeper tutorials, but hopefully this should bootstrap your projects.

Here’s a link to the 1b notebook: https://colab.research.google.com/drive/1mwCy5GQb9xJFSuwt2L_We3eKkVbx2qSt?usp=sharing

1 reply

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burtenshaw

posted an update 3 months ago

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2067

everybody and their dog is fine-tuning Gemma 3 today, so I thought I'd do a longer post on the tips and sharp edges I find. let's go!

1. has to be install everything form main and nightly. this is what I'm working with to get unsloth and TRL running

git+https://github.com/huggingface/transformers@main
git+https://github.com/huggingface/trl.git@main
bitsandbytes
peft

plus this with --no-deps

git+https://github.com/unslothai/unsloth-zoo.git@nightly
git+https://github.com/unslothai/unsloth.git@nightly

2. will brown's code to turn GSM8k into a reasoning dataset is a nice toy experiment https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb

3. with a learning rate of 5e-6 rewards and loss stayed flat for the first 100 or so steps.

4. so far none of my runs have undermined the outputs after 1 epoch. therefore, I'm mainly experimenting with bigger LoRA adapters.

from trl import GRPOConfig

training_args = GRPOConfig(
    learning_rate = 5e-6,
    adam_beta1 = 0.9,
    adam_beta2 = 0.99,
    weight_decay = 0.1,
    warmup_ratio = 0.1,
    lr_scheduler_type = "cosine",
    optim = "adamw_8bit",
    logging_steps = 1,
    per_device_train_batch_size = 2,
    gradient_accumulation_steps = 1,
    num_generations = 2,
    max_prompt_length = 256,
    max_completion_length = 1024 - 256,
    num_train_epochs = 1,
    max_steps = 250,
    save_steps = 250,
    max_grad_norm = 0.1,
    report_to = "none",
)

5. vision fine-tuning isn't available in TRL's GRPOTrainer, so stick to text datasets. but no need to load the model differently in transformers or Unsloth

from transformers import AutoModelForImageTextToText

model = AutoModelForImageTextToText.from_pretrained("google/gemma-3-4b-it)

if you want an introduction to GRPO, check out the reasoning course, it walks you through the algorithm, theory, and implementation in a smooth way.

reasoning-course

2 replies

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burtenshaw

posted an update 3 months ago

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2159

Here’s a notebook to make Gemma reason with GRPO & TRL. I made this whilst prepping the next unit of the reasoning course:

In this notebooks I combine together google’s model with some community tooling

- First, I load the model from the Hugging Face hub with transformers’s latest release for Gemma 3
- I use PEFT and bitsandbytes to get it running on Colab
- Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k
- Finally, I used TRL’s GRPOTrainer to train the model

Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below.

https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing

4 replies

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burtenshaw

posted an update 3 months ago

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3926

I’m super excited to work with @mlabonne to build the first practical example in the reasoning course.

🔗

reasoning-course

Here's a quick walk through of the first drop of material that works toward the use case:

- a fundamental introduction to reinforcement learning. Answering questions like, ‘what is a reward?’ and ‘how do we create an environment for a language model?’

- Then it focuses on Deepseek R1 by walking through the paper and highlighting key aspects. This is an old school way to learn ML topics, but it always works.

- Next, it takes to you Transformers Reinforcement Learning and demonstrates potential reward functions you could use. This is cool because it uses Marimo notebooks to visualise the reward.

- Finally, Maxime walks us through a real training notebook that uses GRPO to reduce generation length. I’m really into this because it works and Maxime took the time to validate it share assets and logging from his own runs for you to compare with.

Maxime’s work and notebooks have been a major part of the open source community over the last few years. I, like everyone, have learnt so much from them.

burtenshaw

posted an update 4 months ago

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5704

I made a real time voice agent with FastRTC, smolagents, and hugging face inference providers. Check it out in this space:

🔗 burtenshaw/coworking_agent

9 replies

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burtenshaw

posted an update 4 months ago

Post

6468

Now the Hugging Face agent course is getting real! With frameworks like smolagents, LlamaIndex, and LangChain.

🔗 Follow the org for updates

agents-course

This week we are releasing the first framework unit in the course and it’s on smolagents. This is what the unit covers:

- why should you use smolagents vs another library?
- how to build agents that use code
- build multiagents systems
- use vision language models for browser use

The team has been working flat out on this for a few weeks. Led by @sergiopaniego and supported by smolagents author @m-ric .

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