gg-hf-gm (gg-hf-gm)

mlabonne

posted an update about 20 hours ago

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1111

New family of 1B models just dropped!

> LiquidAI/LFM2.5-1.2B-Base: 10T → 28T tokens
> LiquidAI/LFM2.5-1.2B-Instruct: new large-scale multi-stage RL
> LiquidAI/LFM2.5-1.2B-JP: our most polite model
> LiquidAI/LFM2.5-VL-1.6B: multi-image multilingual
> LiquidAI/LFM2.5-Audio-1.5B: 8x times faster, no quality loss

Super proud of this release 🤗

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sergiopaniego

posted an update 5 days ago

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2409

The list of hands-on notebooks (some beginner-friendly!) to get started with fine-tuning using TRL keeps growing!!

• SFT
• GRPO
• Tool calling & agents
• RL environments with OpenEnv
• LLMs and VLMs
✨ Many run on FREE Colab, making it super easy to get started fast!

https://github.com/huggingface/trl/tree/main/examples/notebooks

danielhanchen

posted an update 7 days ago

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566

Run Qwen-Image-2512, the new SOTA text-to-image model! 💜

It's the top performing open diffusion model and has more realistic + accurate images/text.

Run locally with 14GB RAM via our Dynamic GGUF: unsloth/Qwen-Image-2512-GGUF

Guide: https://unsloth.ai/docs/models/qwen-image-2512

2 replies

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sergiopaniego

posted an update 8 days ago

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As the year comes to an end, it’s a good moment to catch up on some of the best long-form pieces published by the @huggingface team.

I’ve gathered them all here if you want to read or save them for later:
https://huggingface.co/collections/sergiopaniego/research-and-long-form-blog-posts

sergiopaniego

posted an update 9 days ago

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2195

This super detailed tutorial by @Paulescu is pure gold 🪙 "Fine-tuning a Small Language Model for browser control with GRPO and OpenEnv"

LFM2-350M ( @LiquidAI ) + BrowserGym (OpenEnv) + GRPO (TRL) for learning browser control 🤝

https://paulabartabajo.substack.com/p/fine-tuning-lfm2-350m-for-browser

danielhanchen

posted an update 15 days ago

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3850

You can now run GLM-4.7, the new 355B parameter SOTA model on your local device (128GB RAM).✨

The model achieves SOTA performance on coding, agentic and chat benchmarks.

GGUF: unsloth/GLM-4.7-GGUF
Guide: https://docs.unsloth.ai/models/glm-4.7

3 replies

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sergiopaniego

posted an update 15 days ago

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419

if you’re on holidays 🎄 and want some reading, here are blogs I contributed to this year:

🦄 VLMs in TRL: https://huggingface.co/blog/trl-vlm-alignment
🦖 VLMs in 2025: https://huggingface.co/blog/vlms-2025
👾 tokenization in transformers v5: https://huggingface.co/blog/tokenizers
🛸 faster transformers: https://huggingface.co/blog/faster-transformers

sergiopaniego

posted an update 16 days ago

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1943

The Christmas holidays are here! 🎄
Thinking about learning something new in AI?

@huggingface offers 12 FREE courses covering all the relevant topics, for every level of experience. A great challenge for the holidays (and worth saving for later 🙄)

Let’s explore them!

🧠 𝗟𝗟𝗠 𝗖𝗼𝘂𝗿𝘀𝗲: large language models with HF tools
https://huggingface.co/learn/llm-course

🤖 𝗔𝗴𝗲𝗻𝘁𝘀 𝗖𝗼𝘂𝗿𝘀𝗲: build and deploy AI agents
https://huggingface.co/learn/agents-course

🎨 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲: diffusion models with 🤗 Diffusers
https://huggingface.co/learn/diffusion-course

🔊 𝗔𝘂𝗱𝗶𝗼 𝗖𝗼𝘂𝗿𝘀𝗲: transformers for audio tasks
https://huggingface.co/learn/audio-course

🎮 𝗗𝗲𝗲𝗽 𝗥𝗟 𝗖𝗼𝘂𝗿𝘀𝗲: deep reinforcement learning
https://huggingface.co/learn/deep-rl-course

👁️ 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲: modern computer vision with HF
https://huggingface.co/learn/computer-vision-course

🦾 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 (𝗟𝗲𝗥𝗼𝗯𝗼𝘁): learning-based robotics
https://huggingface.co/learn/robotics-course

🧩 𝗠𝗖𝗣 𝗖𝗼𝘂𝗿𝘀𝗲: Model Context Protocol explained
https://huggingface.co/learn/mcp-course

🧪 𝗔 𝗦𝗺𝗼𝗹 𝗖𝗼𝘂𝗿𝘀𝗲: post-training AI models
https://huggingface.co/learn/a-smol-course

🕹️ 𝗠𝗟 𝗳𝗼𝗿 𝗚𝗮𝗺𝗲𝘀: AI in game development
https://huggingface.co/learn/ml-for-games-course

🧊 𝗠𝗟 𝗳𝗼𝗿 𝟯𝗗: machine learning for 3D data
https://huggingface.co/learn/ml-for-3d-course

📘 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗖𝗼𝗼𝗸𝗯𝗼𝗼𝗸: practical AI notebooks
https://huggingface.co/learn/cookbook

All of them can be found here: https://huggingface.co/learn

danielhanchen

posted an update 19 days ago

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2313

Google releases FunctionGemma, a new 270M parameter model that runs on just 0.5 GB RAM.✨

Built for tool-calling, run locally on your phone at 50+ tokens/s, or fine-tune with Unsloth & deploy to your phone.

GGUF: unsloth/functiongemma-270m-it-GGUF
Docs + Notebook: https://docs.unsloth.ai/models/functiongemma