---
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
inference: false
language:
- fr
- it
- de
- es
- en
license: apache-2.0
model_creator: Mistral AI_
model_name: Mixtral 8X7B Instruct v0.1
model_type: mixtral
prompt_template: '[INST] {prompt} [/INST]

  '
quantized_by: OptimizeLLM
---

This is Mistral AI's Mixtral Instruct v0.1 model, quantized on 02/24/2024. It works well.

# How to quantize your own models with Windows and an RTX GPU:

## Requirements:
* git 
* python

# Instructions:
The following example starts at the root of D drive and quantizes mistral's Mixtral-9x7B-Instruct-v0.1.

## Windows command prompt - folder setup and git clone llama.cpp
* D:
* mkdir Mixtral
* git clone https://github.com/ggerganov/llama.cpp

## Download llama.cpp
Assuming you want CUDA for your NVIDIA RTX GPU(s) use the links below, or grab latest compiled executables from https://github.com/ggerganov/llama.cpp/releases

### Latest version as of Feb 24, 2024:
* https://github.com/ggerganov/llama.cpp/releases/download/b2253/cudart-llama-bin-win-cu12.2.0-x64.zip
* https://github.com/ggerganov/llama.cpp/releases/download/b2253/llama-b2253-bin-win-cublas-cu12.2.0-x64.zip

Extract the two .zip files directly into the llama.cpp folder you just git cloned. Overwrite files as prompted.

## Download Mixtral
* Download the full-blast version of the model by downloading all .safetensors, .json, and .model files to D:\Mixtral\:
* https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1


## Windows command prompt - Convert the model to fp16:
* D:\llama.cpp>python convert.py D:\Mixtral --outtype f16 --outfile D:\Mixtral\Mixtral-8x7B-Instruct-v0.1.fp16.bin

## Windows command prompt - Quantize the fp16 model to q5_k_m:
* D:\llama.cpp>quantize.exe D:\Mixtral\Mixtral-8x7B-Instruct-v0.1.fp16.bin D:\Mixtral\Mixtral-8x7B-Instruct-v0.1.q5_k_m.gguf q5_k_m

That's it!