Codellama 7B Python GGUF

🚀 CodeLlama 7B Python - GGUF

This repository offers GGUF format model files for Meta's CodeLlama 7B Python, which is designed for general code synthesis and understanding, especially in Python programming.

🚀 Quick Start

Model Information

Prompt Template

[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:
{prompt}
[/INST]

✨ Features

• GGUF Format: GGUF is a new format introduced by the llama.cpp team on August 21st, 2023. It replaces GGML, offering better tokenization, support for special tokens, metadata, and extensibility.
• Multiple Quantization Options: Various quantization methods are available, allowing users to choose based on their specific requirements for size, quality, and performance.
• Wide Compatibility: Compatible with many third - party UIs and libraries, including llama.cpp, text - generation - webui, KoboldCpp, etc.

📦 Installation

Downloading GGUF Files

• Using Clients/Libraries: Tools like LM Studio, LoLLMS Web UI, and Faraday.dev can automatically download models and provide a list of available models to choose from.
• In text - generation - webui: Under Download Model, enter the model repo TheBloke/CodeLlama-7B-Python-GGUF and a specific filename (e.g., codellama-7b-python.q4_K_M.gguf), then click Download.
• On the Command Line:
  • First, install the huggingface - hub Python library:

pip3 install huggingface-hub>=0.17.1

- Then download an individual model file:

huggingface-cli download TheBloke/CodeLlama-7B-Python-GGUF codellama-7b-python.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

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

huggingface-cli download TheBloke/CodeLlama-7B-Python-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

- To accelerate downloads on fast connections, install `hf_transfer`:

pip3 install hf_transfer

- And set the environment variable:

HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/CodeLlama-7B-Python-GGUF codellama-7b-python.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 codellama-7b-python.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:\n{prompt}\n[/INST]"

• Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
• Change -c 4096 to the desired sequence length.
• If you want a chat - style conversation, replace the -p <PROMPT> argument with -i -ins.

How to Run in text - generation - webui

Refer to the instructions here: [text - generation - webui/docs/llama.cpp.md](https://github.com/oobabooga/text - generation - webui/blob/main/docs/llama.cpp.md).

How to Run from Python Code

You can use GGUF models from Python using the [llama - cpp - python](https://github.com/abetlen/llama - cpp - python) or ctransformers libraries.

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("TheBloke/CodeLlama-7B-Python-GGUF", model_file="codellama-7b-python.q4_K_M.gguf", model_type="llama", gpu_layers=50)

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

How to Use with LangChain

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

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

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

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d36d5be95a0d9088b674dbb27354107221. 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.

🔧 Technical Details

Code Llama is an auto - regressive language model that uses an optimized transformer architecture. It has been trained between January 2023 and July 2023. The model takes text as input and generates text as output.

📄 License

The use of this model is governed by the llama2 license.

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

If you're able and willing to contribute, it will be most gratefully received and will help to keep providing more models and 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: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann - Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov

Thank you to all generous patrons and donaters! And thank you again to a16z for their generous grant.