Meta Llama 3 70B Instruct GGUF

🚀 Llama 3 70B Instruct - GGUF

This repository contains GGUF format model files for Meta's Llama 3 70B Instruct, offering enhanced performance and features.

📋 Model Information

📚 Documentation

📖 About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st, 2023. It replaces GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenization and support for special tokens. It also supports metadata and is designed to be extensible.

Here is an incomplete list of clients and libraries 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.

📥 How to download GGUF files

⚠️ Important 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: PawanKrd/Llama-3-70B-Instruct-GGUF and below it, a specific filename to download, such as: llama-3-70b-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>=0.17.1

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

huggingface-cli download PawanKrd/Llama-3-70B-Instruct-GGUF llama-3-70b-instruct.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

huggingface-cli download PawanKrd/Llama-3-70B-Instruct-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

pip3 install hf_transfer

HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PawanKrd/Llama-3-70B-Instruct-GGUF llama-3-70b-instruct.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

💻 Usage Examples

🔨 Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d36d5be95a0d9088b674dbb27354107221 or later.

./main -ngl 32 -m llama-3-70b-instruct.Q4_K_M.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"

• Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
• Change -c 8192 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 here: text-generation-webui/docs/llama.cpp.md.

🐍 How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.

How to load this model from Python using ctransformers

First install the package

# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers

Simple example code to load one of these GGUF models

from ctransformers import AutoModelForCausalLM

# 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 = AutoModelForCausalLM.from_pretrained("PawanKrd/Llama-3-70B-Instruct-GGUF", model_file="llama-3-70b-instruct.Q4_K_M.gguf", model_type="llama", gpu_layers=50)

print(llm("AI is going to"))

🤖 How to use with LangChain

Here's guides on using llama-cpp-python or ctransformers with LangChain:

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

👥 Discord

Pawan.Krd's Discord server

🙏 Credits

This README file was initially created by TheBlok and has been modified for this repository.

📄 License

Llama2