Mixtral 8x7B Instruct v0.1 AWQ INT4 EU AI Act Compliance Profile

[!IMPORTANT] This repository is a community-driven quantized version of the original model mistralai/Mixtral-8x7B-Instruct-v0.1 which is the BF16 half-precision official version released by Mistral AI.

Model Information

The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks they tested.

For full details of this model please read our release blog post.

This repository contains mistralai/Mixtral-8x7B-Instruct-v0.1 quantized using AutoAWQ from FP16 down to INT4 using the GEMM kernels performing zero-point quantization with a group size of 128.

Model Usage

[!NOTE] In order to run the inference with Mixtral 8x7B Instruct AWQ in INT4, around 24 GiB of VRAM are needed only for loading the model checkpoint, excluding the KV cache and/or the CUDA graphs, meaning that there should be a bit over that VRAM available.

In order to use the current quantized model, support is offered for different solutions as transformers, autoawq, or text-generation-inference.

🤗 Transformers

In order to run the inference with Mixtral 8x7B Instruct AWQ in INT4, you need to install the following packages:

pip install -q --upgrade transformers autoawq accelerate

To run the inference on top of Mixtral 8x7B Instruct AWQ in INT4 precision, the AWQ model can be instantiated as any other causal language modeling model via AutoModelForCausalLM and run the inference normally.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig

model_id = "hugging-quants/Mixtral-8x7B-Instruct-v0.1-AWQ-INT4"

quantization_config = AwqConfig(
    bits=4,
    fuse_max_seq_len=512, # Note: Update this as per your use

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