Welcome Le Chonk: Mistral Large 4 on Hugging Face

Mistral Large 4 (aka Le Chonk) is a multimodal, multilingual, Mixture-of-Experts (MoE) model from Mistral. It takes text and images as input and generates text. It has about 1T total, 49B active parameter, and a context window of 1M tokens.

The model is released in two official variants:

1. FP8
2. NVFP4

There are also GGUFs created by Unsloth and the llama.cpp team. It’s distributed under the Mistral Ecosystem Open-Weight License (an MIT derivative that establishes limitations for model-as-a-service offerings).

Mistral Large 4 is particularly strong for cybersecurity, coding agents, and multimodality. In this post, we look at how the model works, its capabilities, architecture and more!

Table of Contents

• "Welcome Le Chonk: Mistral Large 4 on Hugging Face"
  • Table of Contents
  • What is new with Mistral Large 4
    • Cybersecurity Capabilities
  • Architecture
    • Vision encoder
  • Benchmark Results
  • Inference Support
    • Transformers
    • vLLM
    • Llama.cpp
  • Fine-tuning with TRL
  • Inference Endpoints
  • Dell Enterprise Hub
  • Try it out

What is new with Mistral Large 4

Mistral Large 4 combines a large pool of experts with sparse attention and a Pixtral vision encoder. The language model selects a small group of experts for each token, while its attention indexer selects which earlier token positions to attend to. These are two different forms of sparsity, where one selects feed-forward networks, the other selects context positions.

You can find all the checkpoints in the Mistral Large 4 collection.

Cybersecurity Capabilities

In short, cyber capabilities like ML4’s in the open are a huge boost for the community.

We have interviewed Pierre Stock about the cyber capabilities of the model, you can watch it here.

Architecture

Regarding RoPE, Mistral uses LeRope, a YaRN-scaled RoPE with one learned multiplier per frequency.

Vision encoder

Benchmark Results

Inference Support

Transformers

To run the model, upgrade the transformers package

pip install --upgrade transformers

torchrun --nproc-per-node 8 script.py