Sauerkrautlm Mixtral 8x7B GGUF

🚀 SauerkrautLM Mixtral 8X7B - GGUF

This repository offers GGUF format model files for SauerkrautLM Mixtral 8X7B, a powerful text - generation model with support for multiple languages.

🚀 Quick Start

This README provides comprehensive information about the SauerkrautLM Mixtral 8X7B model in GGUF format, including details about the model, compatibility, how to download and run the model, and more.

✨ Features

• Multilingual Support: Supports English (en), German (de), French (fr), Italian (it), and Spanish (es).
• Text Generation: Ideal for various text - generation tasks.
• Multiple Quantization Options: Offers different quantization methods to balance between model size and quality.

📦 Installation

The installation mainly involves downloading the appropriate GGUF model file. Different clients and libraries can be used for this purpose, and the following sections will provide detailed guidance.

💻 Usage Examples

Basic Usage

Example llama.cpp command

./main -ngl 35 -m sauerkrautlm-mixtral-8x7b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"

• 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.

Advanced Usage

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

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="./sauerkrautlm-mixtral-8x7b.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(
  "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # 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="./sauerkrautlm-mixtral-8x7b.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."
        }
    ]
)

📚 Documentation

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

• llama.cpp. The source project for GGUF. Offers a CLI and a server option.
• [text - generation - webui](https://github.com/oobabooga/text - generation - webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
• KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
• GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
• LM Studio, an easy - to - use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
• [LoLLMS Web UI](https://github.com/ParisNeo/lollms - webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
• Faraday.dev, an attractive and easy to use character - based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
• [llama - cpp - python](https://github.com/abetlen/llama - cpp - python), a Python library with GPU accel, LangChain support, and OpenAI - compatible API server.
• candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
• ctransformers, a Python library with GPU accel, LangChain support, and OpenAI - compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Repositories available

• [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SauerkrautLM - Mixtral - 8x7B - AWQ)
• [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SauerkrautLM - Mixtral - 8x7B - GPTQ)
• [2, 3, 4, 5, 6 and 8 - bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SauerkrautLM - Mixtral - 8x7B - GGUF)
• [VAGO solutions's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/VAGOsolutions/SauerkrautLM - Mixtral - 8x7B)

Prompt template: ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d

They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

Explanation of quantisation methods

Provided files

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

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: TheBloke/SauerkrautLM - Mixtral - 8x7B - GGUF and below it, a specific filename to download, such as: sauerkrautlm - mixtral - 8x7b.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 TheBloke/SauerkrautLM - Mixtral - 8x7B - GGUF sauerkrautlm - mixtral - 8x7b.Q4_K_M.gguf --local - dir . --local - dir - use - symlinks False

huggingface - cli download TheBloke/SauerkrautLM - Mixtral - 8x7B - GGUF --local - dir . --local - dir - use - symlinks False --include='*Q4_K*gguf'

pip3 install hf_transfer

HF_HUB_ENABLE_HF_TRANSFER = 1 huggingface - cli download TheBloke/SauerkrautLM - Mixtral - 8x7B - GGUF sauerkrautlm - mixtral - 8x7b.Q4_K_M.gguf --local - dir . --local - dir - use - symlinks False

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](https://github.com/oobabooga/text - generation - webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

How to use with LangChain

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

• LangChain + llama - cpp - python
• LangChain + ctransformers

🔧 Technical Details

The SauerkrautLM Mixtral 8X7B model is based on the Mixtral architecture. It is finetuned and aligned with SFT and DPO techniques. The model supports multiple languages including English, German, French, Italian, and Spanish.

📄 License

This model is licensed under the Apache 2.0 license.

📊 Model Information Table

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from [gpus.llm - utils.org](llm - utils)!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

• Patreon: https://patreon.com/TheBlokeAI
• Ko - Fi: https://ko - fi.com/TheBlokeAI

Special thanks to: Aemon Algiz.

Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel - Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann - Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: VAGO solutions's SauerkrautLM Mixtral 8X7B


VAGO solutions SauerkrautLM - Mixtral - 8x7B

Introducing SauerkrautLM - Mixtral - 8x7B – our Sauerkraut version of the powerful Mixtral - 8x7B! Finetuned and aligned with SFT and DPO

Table of Contents

1. [Overview of all SauerkrautLM - Mixtral models](#all - sauerkrautlm - mixtral - models)
2. [Model Details](#model - details)
   • [Prompt template](#prompt - template)
   • [Training Dataset](#training - dataset)
3. Evaluation
4. Disclaimer
5. Contact
6. Collaborations
7. Acknowledgement

All SauerkrautLM - Mixtral Models

Model Details

SauerkrautLM - Mixtral - 8x7B

• Model Type: SauerkrautLM - Mixtral - 8x7B is a Mixture of Experts (MoE) Model based on [mistralai/Mixtral - 8x7B - v0.1](https://huggingface.co/mistralai/Mixtral - 8x7B - v0.1)
• Language(s): English, German, French, Italian, Spanish
• License: APACHE 2.0
• Contact: [Website](https://vago - solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago - solutions.de)