--- 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 --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://pbs.twimg.com/profile_banners/1633782755669708804/1678359514/1500x500" alt="Inferless" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <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> </div> <!-- <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> --> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;"><a href="https://0ooatrmbp25.typeform.com/to/nzuhQtba"><b>Join Private Beta</b></a></p></div> <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> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mixtral-8x7B - GPTQ - Model creator: [Mistralai](https://huggingface.co/mistralai) - Original model: [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) <!-- description start --> ## 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 <!-- description end --> <!-- repositories-available start --> ## 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 <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> <!-- How to use start --> ## 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" ```