README.md

---
base_model: mistralai/Mixtral-8x7B-v0.1
inference: false
language:
- en
license: apache-2.0
model-index:
- name: Mixtral-8x7B
  results: []
model_creator: mistralai
model_name: Mixtral-8x7B
model_type: mixtral
prompt_template: |
  <|im_start|>system
  {system_message}<|im_end|>
  <|im_start|>user
  {prompt}<|im_end|>
  <|im_start|>assistant
quantized_by: Inferless
tags:
- mixtral
- vllm
- GPTQ
---
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        <p style="margin-top: 0.5em; margin-bottom: 0em;">Serverless GPUs to scale your machine learning inference without any hassle of managing servers, deploy complicated and custom models with ease.</p>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">Go through <a href="https://tutorials.inferless.com/deploy-quantized-version-of-solar-10.7b-instruct-using-inferless">this tutorial</a>, for quickly deploy <b>Mixtral-8x7B-v0.1</b> using Inferless</p></div>
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# Mixtral-8x7B - GPTQ
- Model creator: [Mistralai](https://huggingface.co/mistralai)
- Original model: [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)

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## Description

This repo contains GPTQ model files for [Mistralai's Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1).

### About GPTQ

GPTQ is a method that compresses the model size and accelerates inference by quantizing weights based on a calibration dataset, aiming to minimize mean squared error in a single post-quantization step. GPTQ achieves both memory efficiency and faster inference.

It is supported by:

- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

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## Shared files, and GPTQ parameters

Models are released as sharded safetensors files.

| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.96 GB

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## How to use
You will need the following software packages and python libraries:
```json
build:
  cuda_version: "12.1.1"
  system_packages:
    - "libssl-dev"
  python_packages:
    - "torch==2.1.2"
    - "vllm==0.2.6"
    - "transformers==4.36.2"
    - "accelerate==0.25.0"
```