New Blog📖✨: nanoVLM: The simplest way to train your own Vision-Language Model in pure PyTorch explained step-by-step! Easy to read, even easier to use. Train your first VLM today!

Real-time SmolVLM in a web browser with transformers.js. All running locally with no installs. Just open the website.

Today, we share the tech report for SmolVLM: Redefining small and efficient multimodal models. 🔥 Explaining how to create a tiny 256M VLM that uses less than 1GB of RAM and outperforms our 80B models from 18 months ago! huggingface.co/papers/2504....

🚀 We just dropped SmolDocling: a 256M open-source vision LM for complete document OCR! 📄✨ Lightning fast, process a page in 0.35 sec on consumer GPU using < 500MB VRAM ⚡ SOTA in document conversion, beating every competing model we tested (including models 27x more params) 🤯 But how? 🧶⬇️

Extremely bullish on @CohereForAI's Aya Vision (8B & 32B) - new SOTA open-weight VLMs - 8B wins up to 81% of the time in its class, better than Gemini Flash - 32B beats Llama 3.2 90B! - Integrated on @hf.co from Day 0! Check out their blog! huggingface.co/blog/aya-vis...

Fuck it, today we're open-sourcing the codebase used to train SmolVLM from scratch on 256 H100s 🔥 Inspired by our team's effort to open-source DeepSeek's R1, we are releasing the training and evaluation code on top of the weights 🫡 Now you can train any SmolVLM—or create your own custom VLMs!

Introducing the smollest VLMs yet! 🤏 SmolVLM (256M & 500M) runs on <1GB GPU memory. Fine-tune it on your laptop and run it on your toaster. 🚀 Even the 256M model outperforms our Idefics 80B (Aug '23). How small can we go? 👀

We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute 🔥 How? By combining step-wise reward models with tree search algorithms :) We're open sourcing the full recipe and sharing a detailed blog post 👇

Do you want to try Llama 3.3? huggingface.co/chat/

Just been messing with SmolVLM - visual language model that is _Smol_. 2B params, does some amazing stuff, with little memory, quickly. I'll post a couple of examples below. Super cool stuff from @merve.bsky.social & @andimara.bsky.social!

📬 Summarize and rewrite your text/emails faster, and offline! Check @andimara.bsky.social's Smol Tools for summarization and rewriting. It uses SmolLM2 to summarize text and make it more friendly or professional, all running locally thanks to llama.cpp github.com/huggingface/...

it only takes a single CLI command to kick-off a Direct Preference Optimization fine-tuning run on SmolVLM huggingface.co/blog/smolvlm... you're welcome

Do you want to help us build SmolVLM2? We are hiring interns! Remote roles from EMEA or US. apply.workable.com/huggingface/...

Let's go! We are releasing SmolVLM, a smol 2B VLM built for on-device inference that outperforms all models at similar GPU RAM usage and tokens throughputs. SmolVLM can be fine-tuned on a Google collab and be run on a laptop! Or process millions of documents with a consumer GPU!

The authors of ColPali trained a retrieval model based on SmolVLM 🤠 TLDR; - ColSmolVLM performs better than ColPali and DSE-Qwen2 on all English tasks - ColSmolVLM is more memory efficient than ColQwen2 💗 Find the model here huggingface.co/vidore/colsm...

🚀 Excited to share that our provocation paper "Evaluations Using Wikipedia without Data Contamination: From Trusting Articles to Trusting Edit Processes" has been accepted at the NeurIPS workshop Evaluating Evaluations! 🌐📚 #NeurIPS2024 #evaleval #AIEvaluation

It's pretty sad to see the negative sentiment towards Hugging Face on this platform due to a dataset put by one of the employees. I want to write a small piece. 🧵 Hugging Face empowers everyone to use AI to create value and is against monopolization of AI it's a hosting platform above all.

Some people are pushing models to the top right of the plot following the scaling laws, others push them to the top left and make them faster and cheaper! We need both!

Let's go! We are releasing SmolVLM, a smol 2B VLM built for on-device inference that outperforms all models at similar GPU RAM usage and tokens throughputs. SmolVLM can be fine-tuned on a Google collab and be run on a laptop! Or process millions of documents with a consumer GPU!

The best thing about working at @huggingface is how often I’m floored by the brilliance of my colleagues. Truly inspiring.

Working hard on an awesome multimodal release. Coming soon :)