Codellama 70B Instruct GGUF

🚀 Codellama 70B Instruct - GGUF

This repository provides GGUF format model files for Codellama 70B Instruct, offering various quantization options for different use - cases.

🚀 Quick Start

The following sections will guide you through the details of the Codellama 70B Instruct - GGUF model, including its description, compatibility, provided files, and how to download and run it.

✨ Features

• Multiple Quantization Options: Offers a range of quantization methods such as Q2_K, Q3_K, Q4_K, Q5_K, Q6_K, and Q8_0, allowing users to choose based on their hardware and quality requirements.
• Wide Compatibility: Compatible with many popular clients and libraries like llama.cpp, text - generation - webui, KoboldCpp, etc.
• Split File Support: For large files (Q6_K and Q8_0), split files are provided with instructions on how to join them.

📚 Documentation

Description

This repo contains GGUF format model files for Code Llama's Codellama 70B Instruct. These files were quantised using hardware kindly provided by Massed Compute.

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.
• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8 - bit GGUF models for CPU+GPU inference
• Code Llama's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: CodeLlama - 70B - Instruct

Source: system

  {system_message}<step> Source: user

  {prompt} <step> Source: 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.

Q6_K and Q8_0 files are split and require joining

Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.

cat codellama - 70b - instruct.Q6_K.gguf - split - * > codellama - 70b - instruct.Q6_K.gguf && rm codellama - 70b - instruct.Q6_K.gguf - split - *
cat codellama - 70b - instruct.Q8_0.gguf - split - * > codellama - 70b - instruct.Q8_0.gguf && rm codellama - 70b - instruct.Q8_0.gguf - split - *

COPY /B codellama - 70b - instruct.Q6_K.gguf - split - a + codellama - 70b - instruct.Q6_K.gguf - split - b codellama - 70b - instruct.Q6_K.gguf
del codellama - 70b - instruct.Q6_K.gguf - split - a codellama - 70b - instruct.Q6_K.gguf - split - b

COPY /B codellama - 70b - instruct.Q8_0.gguf - split - a + codellama - 70b - instruct.Q8_0.gguf - split - b codellama - 70b - instruct.Q8_0.gguf
del codellama - 70b - instruct.Q8_0.gguf - split - a codellama - 70b - instruct.Q8_0.gguf - split - b

How to download GGUF files

⚠️ Important Note

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/CodeLlama-70B-Instruct-GGUF and below it, a specific filename to download, such as: codellama - 70b - instruct.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/CodeLlama-70B-Instruct-GGUF codellama - 70b - instruct.Q4_K_M.gguf --local - dir . --local - dir - use - symlinks False

huggingface - cli download TheBloke/CodeLlama-70B-Instruct-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/CodeLlama-70B-Instruct-GGUF codellama - 70b - instruct.Q4_K_M.gguf --local - dir . --local - dir - use - symlinks False

Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d

📄 License

The license for this model is llama2.