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burtenshaw
updated
a
dataset
9 days ago
thomwolf
authored
a
paper
25 days ago
Post
4970
LiquidAI/LFM2-8B-A1B just dropped!
8.3B params with only 1.5B active/token 🚀
> Quality ≈ 3–4B dense, yet faster than Qwen3-1.7B
> MoE designed to run on phones/laptops (llama.cpp / vLLM)
> Pre-trained on 12T tokens → strong math/code/IF
8.3B params with only 1.5B active/token 🚀
> Quality ≈ 3–4B dense, yet faster than Qwen3-1.7B
> MoE designed to run on phones/laptops (llama.cpp / vLLM)
> Pre-trained on 12T tokens → strong math/code/IF
- 1 reply
burtenshaw
authored
a
paper
about 1 month ago
Post
3572
⚛️ New drop of tiny task-specific models!
Want to do data extraction, translation, RAG, tool use, or math on a Raspberry Pi? We got you covered! ✅
These tiny models were fine-tuned to perform narrow tasks extremely well, making them competitive with much larger models.
You can deploy them today on-device or even on GPUs for big data operations!
LiquidAI/liquid-nanos-68b98d898414dd94d4d5f99a
Want to do data extraction, translation, RAG, tool use, or math on a Raspberry Pi? We got you covered! ✅
These tiny models were fine-tuned to perform narrow tasks extremely well, making them competitive with much larger models.
You can deploy them today on-device or even on GPUs for big data operations!
LiquidAI/liquid-nanos-68b98d898414dd94d4d5f99a
- 1 reply
burtenshaw
posted
an
update
about 2 months ago
Post
4844
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
2 months ago
Post
5351
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
2 months ago
Post
3002
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
Post
6764
Liquid just released two 450M and 1.6B param VLMs!
They're super fast and leverage SigLIP2 NaFlex encoders to handle native resolutions without distortion. It's ideal for on-device deployment in constrained environments like phones.
It's available today on Hugging Face, with an inference and a fine-tuning Colab notebooks.
LiquidAI/LFM2-VL-450M
LiquidAI/LFM2-VL-1.6B
They're super fast and leverage SigLIP2 NaFlex encoders to handle native resolutions without distortion. It's ideal for on-device deployment in constrained environments like phones.
It's available today on Hugging Face, with an inference and a fine-tuning Colab notebooks.
LiquidAI/LFM2-VL-450M
LiquidAI/LFM2-VL-1.6B
burtenshaw
posted
an
update
4 months ago
Post
1567
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
Post
5612
LiquidAI
open-sources a new generation of edge LLMs! 🥳Based on a new hybrid architecture, these 350M, 700M, and 1.2B models are both fast and performant, ideal for on-device deployment.
I recommend fine-tuning them to power your next edge application. We already provide Colab notebooks to guide you. More to come soon!
📝 Blog post: https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models
🤗 Models: LiquidAI/lfm2-686d721927015b2ad73eaa38
- 1 reply
burtenshaw
posted
an
update
4 months ago
Post
3094
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. /static-proxy?url=https%3A%2F%2Fendpoints.huggingface.co%2F%3C%2Fa%3E%3Cbr%3E%3Cbr%3E%3Cspan%3E%F0%9F%A6%AB 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. /static-proxy?url=https%3A%2F%2Fendpoints.huggingface.co%2F%3C%2Fa%3E%3Cbr%3E%3Cbr%3E%3Cspan%3E%F0%9F%A6%AB 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.
thomwolf
authored
a
paper
5 months ago
burtenshaw
posted
an
update
5 months ago
Post
1135
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.
burtenshaw
posted
an
update
5 months ago
Post
1640
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
thomwolf
authored
a
paper
5 months ago
burtenshaw
posted
an
update
6 months ago
Post
2732
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
3315
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
2524
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