---
license: apache-2.0
library_name: vllm
pipeline_tag: image-text-to-text
tags:
- int4
- vllm
- llmcompressor
base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
---

# Mistral-Small-3.1-24B-Instruct-2503-GPTQ-4b-128g

## Model Overview

This model was obtained by quantizing the weights of [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.

Only the weights of the linear operators within `language_model` transformers blocks are quantized. Vision model and multimodal projection are kept in original precision. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization.

Model checkpoint is saved in [compressed_tensors](https://github.com/neuralmagic/compressed-tensors) format.

## Usage 

* To use the model in `transformers` update the package to stable release of Mistral-3
  
  `pip install git+https://github.com/huggingface/transformers@v4.49.0-Mistral-3`
* To use the model in `vLLM` update the package to version `vllm>=0.8.0`.