What a boring Jinja snippet tells us about the new Qwen-3 model. The new Qwen-3 model by Qwen ships with a much more sophisticated chat template than its predecessors Qwen-2.5 and QwQ. By taking a look at the differences in the Jinja template, we can find interesting insights into the new model. Chat Templates

Updated! (September 2025) This post has been updated with the latest Gradio MCP features including Resources, Prompts, enhanced authentication, and many more. Gradio is a Python library used by more than 1 million developers    

TLDR: Going forward, we’re aiming for Transformers to be the pivot across frameworks: if a model architecture is supported by transformers, you can expect it to be supported in the rest of the ecosystem. Transformers was created in 2019, shortly following the release of the BERT Transformer model. Since then, we’ve continuously aimed to add state-of-the-art architectures, initially focused on NLP, then growing to Audio and computer vision. Today, transformers is the default library for LLMs and VLMs in the […]

In this blogpost, we present the key highlights and rationales about the Falcon-Edge series – a collection of powerful, universal, and fine-tunable language models available in ternary format, based on the BitNet architecture. Drawing from our experience with BitNet, Falcon-Edge introduces and validates an new pre-training paradigm that delivers a full-scope output from a single training process, simultaneously yielding both non-quantized and quantized model variants. This comprehensive approach produces a non-BitNet model in bfloat16 format, the native BitNet model, and […]

Today at the Microsoft Build conference, Satya Nadella announced an expanded collaboration with Hugging Face, to make its wide diversity of open models easy to deploy on Azure secure infrastructure. If you head over to Azure AI Foundry today, you will find a vastly expanded collection of 10,000+ Hugging Face models you can deploy in a couple clicks to power AI applications working with text, audio and images. And we’re just getting started!

nanoVLM is the simplest way to get started with training your very own Vision Language Model (VLM) using pure PyTorch. It is lightweight toolkit which allows you to launch a VLM training on a free tier colab notebook. We were inspired by Andrej Karpathy’s nanoGPT, and provide a similar project for the vision domain. At its heart, nanoVLM is a toolkit that helps you build and train a model that can understand both images and text, and then generate text […]

Large diffusion models like Flux (a flow-based text-to-image generation model) can create stunning images, but their size can be a hurdle, demanding significant memory and compute resources. Quantization offers a powerful solution, shrinking these models to make them more accessible without drastically compromising performance. But the big question always is: can you actually tell the difference in the final image? Before we dive into the technical details of how various quantization backends in Hugging Face Diffusers work, why not test […]

Check out our official blogpost (EN, AR) We are excited to introduce Falcon-Arabic, a 7B parameter Language Model that sets a new benchmark for Arabic NLP. Built on the Falcon 3 architecture, Falcon-Arabic is a multilingual model that supports Arabic, English, and several other languages. It excels in general knowledge, Arabic grammar, mathematical reasoning, complex problem solving, and understanding the rich diversity of Arabic dialects. Falcon-Arabic supports a context length of 32,000 tokens, allowing it to handle long documents and […]

Introduction Check out also our official blogpost Today, we are proud to introduce the Falcon-H1 series, a collection of six open-source models ranging from 0.5B to 34B parameters, each available in both base and instruction-tuned variants. At the core of these models lies a hybrid architecture that combines the strengths of the classical Transformer-based attention mechanism with the State Space Model (SSM), known    

NEW: tiny-agents now supports AGENTS.md standard. 🥳 Inspired by Tiny Agents in JS, we ported the idea to Python 🐍 and extended the huggingface_hub client SDK to act as a MCP Client so it can pull tools from MCP servers and pass them to the LLM during inference. MCP (Model Context Protocol) is an open protocol that standardizes how Large Language Models (LLMs) interact with external tools and APIs. Essentially, it removed the need to write custom integrations for each […]