metadata

base_model: mistral-community/Mixtral-8x22B-v0.1
inference: false
language:
  - en
license: apache-2.0
model_creator: MODELCREATOR
model_name: MODELNAMEU
model_type: TODO
quantized_by: ChristianAzinn
library_name: transformers
pipeline_tag: text-generation
tags:
  - quantized
  - 2-bit
  - 3-bit
  - 4-bit
  - 5-bit
  - 6-bit
  - 8-bit
  - 16-bit
  - GGUF
  - mixtral
  - moe

mixtral-8x22b-v0.1-imatrix

Model creator: Mistral AI

Original model: Mixtral-8x22B-v0.1.

Original Description

None so far, but check back when Mistral releases the model officially on HF Hub.

Description

This repo contains GGUF format files for the Mixtral-8x22B-v0.1 text generation model.

These files were converted and quantized with llama.cpp on a consumer RTX 4090.

This model supports a 65536 context window.

Quantizer's Notes

Be warned that this model is ENORMOUS and will not run on most consumer hardware! Consider using an I quant (IQ2_XXS, IQ2_XS, IQ3_XS) for better performance per bit.

This repo also contains files uniquely quantized using an importance matrix, which increases response quality at little cost otherwise, supported by llama.cpp as of PR #4861 and supported for K-quants as of PR #4930. The imatrix does nothing to Q8_0, fp16, or fp32, so use another repository for those quantizations.

The files are split in a weird way because llama.cpp/gguf-split bugs out with extra-large models like Mixtral 8x22B. This may be fixed later.

Speaking of which, these files are split because HuggingFace limits file size to 50GB. You may need to merge them using llama.cpp's gguf-split merge functionality, but both llama.cpp and LM Studio (as of latest release) support loading directly from model shards.

The "Original Model" link above links to a repository of HF Transformers-compatible safetensors on the Hub, but the files in this repository (the one whose README you are reading) were quantized directly from a local conversion of the original torrent.

Compatibility

These files are compatible with llama.cpp, LM Studio, Ollama, and any other service built on top of llama.cpp.

Meta-information

Provided Files

There's no good way for me to make a Provided Files section since each is sharded. However, I provide the following quantizations: IQ2_XXS, IQ2_XS, Q2_K, IQ3_XXS, Q3_K_S, Q3_K_M, Q3_K_L, Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M, Q6_K.

Examples

How to download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

LM Studio
LoLLMS Web UI
Faraday.dev

In `text-generation-webui`

Under Download Model, you can enter the model repo: ChristianAzinn/mixtral-8x22b-v0.1-imatrix and below it, a specific filename to download, such as: mixtral-8x22b-v0.1-imatrix.Q4_K_M.gguf.

Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface-cli download ChristianAzinn/mixtral-8x22b-v0.1-imatrix mixtral-8x22b-v0.1-imatrix.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage (click to read)

You can also download multiple files at once with a pattern:

huggingface-cli download ChristianAzinn/mixtral-8x22b-v0.1-imatrix --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ChristianAzinn/mixtral-8x22b-v0.1-imatrix mixtral-8x22b-v0.1-imatrix.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

## Example `llama.cpp` command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m mixtral-8x22b-v0.1-imatrix.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt} [/INST]"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 32768 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in `text-generation-webui`

Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

How to load this model in Python code, using llama-cpp-python

For full documentation, please see: llama-cpp-python docs.

First install the package

Run one of the following commands, according to your system:

# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python

Simple llama-cpp-python example code

from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./mistral-7b-instruct-v0.2.Q4_K_M.gguf",  # Download the model file first
  n_ctx=32768,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
  "<s>[INST] {prompt} [/INST]", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./mistral-7b-instruct-v0.2.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)

How to use with LangChain

Here are guides on using llama-cpp-python and ctransformers with LangChain:

How to use with LM Studio

Here are guides on using LM Studio to serve this model. The chat UI is very intuitive to get up and running, but don't be afraid to ask in the Discord if you have any trouble. LM Studio also supports serving with the OpenAI API format, running multiple models at once, and serving embedding models.

Acknowledgements

This README is inspired by those of the legendary TheBloke. They say imitation is the sincerest form of flattery, and I've elected to reuse his "Examples" section because he did it very well and I see no reason to reinvent the wheel.