Claire 7B 0.1 GGUF

🚀 Claire 7B 0.1 - GGUF

This repository offers GGUF format model files for OpenLLM France's Claire 7B 0.1, facilitating various text - generation tasks.

📦 Model Information

👀 Visual Introduction

📖 Description

This repo contains GGUF format model files for OpenLLM France's Claire 7B 0.1.

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, 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.
• LM Studio, an easy - to - use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
• LoLLMS Web UI, 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.
• ctransformers, a Python library with GPU accel, LangChain support, and OpenAI - compatible AI server.
• 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.

📂 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
• OpenLLM France's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

📝 Prompt template: OpenLLM - France

- Bonjour BotName, {prompt}
- Bonjour UserName,

🛠️ 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

⚠️ 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/Claire-7B-0.1-GGUF and below it, a specific filename to download, such as: claire-7b-0.1.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/Claire-7B-0.1-GGUF claire-7b-0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

huggingface-cli download TheBloke/Claire-7B-0.1-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/Claire-7B-0.1-GGUF claire-7b-0.1.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 or later.

./main -ngl 32 -m claire-7b-0.1.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "- Bonjour BotName, {prompt}\n- Bonjour UserName,"

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

Change -c 2048 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.

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.