🧠 Following Hugging Face's blog on scaling test-time compute with open models—letting models "think longer," inspired by OpenAI & DeepMind—I created a recipe to extend inference time for Instruct LLMs, tackling harder tasks like complex math problems.

Links below 👇

I’m a big fan of smol models—compact, efficient, and perfect for inference/training on limited resources. Even better when they’re multimodal! 🤏✨

I explored fine-tuning SmolVLM, a multimodal smol model using TRL with SFT and DPO, creating 2 hands-on projects!

🔗Links below👇

💡I've been exploring how to go smol with multimodal RAG.

I've created a project using SmolVLM and ColSmolVLM to create a multimodal RAG that can run on Colab's free tier.

Featuring:
🤏👀 SmolVLM (VLM)
🤏📚ColQwen2 (Doc Retrieval)
⚙️ Runs in Colab's free-tier GPU

Link below

💡 New Multimodal RAG Recipe with Re-Ranking 💡

I explored how to enhance a multimodal RAG pipeline by integrating a re-ranker!

Featuring:
✨ Qwen2-VL-7B (VLM)
📚 ColQwen2 (Doc Retrieval)
🔍 MonoQwen2 (Re-ranking)
🔥 Optimized for consumer GPUs with quantized VLMs.

Link below:

Learn how to build a complete multimodal RAG pipeline with
ColQwen2 as retriever, MonoQwen2-VL as reranker, Qwen2-VL as VLM in this notebook that runs on a GPU as small as L4 🔥 huggingface.co/learn/cookbo...

✨ Gave a talk on autonomous driving today to undergrad students! We covered everything from definitions to real-world examples, plus cutting-edge concepts like Generative World Models and Vision-Language Models (VLMs). Exciting future ahead! 🚗💡

This is such a cool project, and it was a truly exciting experience to contribute to it!! 😀

We took those TRL notebooks from last week and made a page from them. So if you're upskilling on finetuning or aligning LLMs, and want examples from the community (like Maxime Labonne Philipp Schmid Sergio Paniego Blanco), check it out!

bsky.app/profile/benb...

>> huggingface.co/docs/trl/mai...

I've been exploring the latest Llama 3.2 releases and working on a couple of projects you may find interesting:

1️⃣ Understanding tool calling with Llama 3.2 🔧
2️⃣ Using Text Generation Inference (TGI) with Llama models 🦙

(links in the next post)

💡 A few days ago, I came across a fascinating post about Agentic RAG by Erika Cardenas and Leonie Monigatti, and it inspired me to dive into the concept and bring it to life in code!

4/6 More vision skills for complex visual tasks. This tutorial shows how to fine-tune the Qwen2-VL-7B model for visual question answering using the ChartQA dataset.

huggingface.co/learn/cookbo...

by @sergiopaniego.bsky.social

TRL is a cornerstone of LLM post training and imo it's the default to learn.

There are great alternatives like Unsloth, Axolotl, and AutoTrain. But if you want a daily drive that does experimentation to production, it's TRL.

🧵 these community notebooks guide you through TRL's core:

hola 👋 hi 👋