Llama 2 Cpp Github

Contributing to ggerganovllamacpp

Introduction

Contribute to ggerganovllamacpp development by creating an account on GitHub. The main goal of llamacpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide variety of hardware - locally and in the cloud.

Key Features

• Plain CC implementation without any dependencies
• Apple silicon is a first-class citizen - optimized via ARM NEON Accelerate and Metal frameworks.
• Have you ever wanted to inference a baby Llama 2 model in pure C? With this code you can train the Llama 2 LLM architecture from scratch in PyTorch then save the weights to a raw binary file then load that into one simple 425-line C file runcpp that inferences the model simply in fp32 for now.
• This project llama2cpp is derived from the llama2c project and has been entirely rewritten in pure C. Its specifically designed for performing inference for the llama2 and other GPT models without any environmental dependencies. The transition to C enhances the codes readability and extensibility.
• This release includes model weights and starting code for pre-trained and fine-tuned Llama language models ranging from 7B to 70B parameters. This repository is intended as a minimal example to load Llama 2 models and run inference. For more detailed examples leveraging Hugging Face see llama-recipes.
• Open source free for research and commercial use. Were unlocking the power of these large language models. Our latest version of Llama, Llama 2, is now accessible to individuals, creators, researchers, and businesses so they can experiment, innovate, and scale their ideas responsibly.
• Llama 2 is a family of state-of-the-art open-access large language models released by Meta today and were excited to fully support the launch with comprehensive integration in Hugging Face.
• Some differences between the two models include:
  
  • Llama 1 released 7, 13, 33, and 65 billion parameters while Llama 2 has7, 13, and 70 billion parameters
  • Llama 2 was trained on 40% more data
  • Llama2 has double the context length
  • Llama2 was fine-tuned for helpfulness and safety
• Please review the research paper and model cards llama 2.
• Llamacpp s objective is to run the LLaMA model with 4-bit integer quantization on MacBook. It is a plain CC implementation optimized for Apple silicon and x86.
• Code Llama is a family of state-of-the-art open-access versions of Llama 2 specialized on code tasks and were excited to release integration in the Hugging Face ecosystem. Code Llama has been released with the same permissive community license as Llama 2 and is available for commercial use.
• This repository provides a set of ROS 2 packages to integrate llamacpp into ROS 2. By using the llama_ros packages you can easily incorporate the powerful optimization capabilities of llamacpp into your ROS 2 projects by running GGUF.
• Request the access to download the llama2 model from the meta ai website.
• Unlock ultra-fast performance on your fine-tuned LLM Language Learning Model using the Llamacpp library on local hardware like PCs and Macs. Lets dive into a tutorial that navigates.
• 202403 bigdl-llm has now become ipex-llm see the migration guide here. You may find the original BigDL project here.
• 202402 ipex-llm now supports directly loading model from ModelScope
• 202402 ipex-llm added inital INT2 support based on llamacpp IQ2 mechanism which makes it possible to run large-size LLM eg Mixtral-8x7B on Intel.
  • N_batch512 Should be between 1 and n_ctx consider the amount of VRAM in your GPU
  • N_gpu_layers32 Change this value based on.
• Get started developing applications for WindowsPC with the official ONNX Llama 2 repo here and ONNX runtime here. Note that to use the ONNX Llama 2 repo you will need to submit a request to download model artifacts from sub-repos. This request will be reviewed by the Microsoft ONNX team.
• In this blog post we explored how to use the llamacpp library in Python with the llama-cpp-python package. These tools enable high-performance CPU-based execution of LLMs. Llamacpp is updated almost every day. The speed of inference is getting better and the community regularly adds support for new models. So the project is young and moving quickly. Hat tip to the awesome llamacpp for inspiring this project. Compared to llamacpp I wanted something super simple minimal and educational so I chose to hard-code the Llama 2 architecture and just roll one inference file of pure C with no dependencies.
• Mamba in llamacpp uses 1 KV cell per sequence well probably need to introduce some other tensor lists than k_l and v_l in llama_kv_cache to avoid conflicting with attentions one KV cells per token a different set of cells will be required and yet another session file format revision.

Prerequisites

To contribute to the ggerganovllamacpp project, you will need the following:

• A GitHub account
• A compiler that supports C++17
• A text editor or IDE

Getting Started

To get started, follow these steps:

1. Fork the ggerganovllamacpp repository. Click the "Fork" button on the GitHub page for the repository. This will create a copy of the repository in your own account.
2. Clone your forked repository to your local machine. Open your terminal and run the following command:
   git clone https://github.com/YOUR_USERNAME/ggerganovllamacpp.git
3. Navigate to the ggerganovllamacpp directory. Open your terminal and run the following command:
   cd ggerganovllamacpp
4. Create a new branch for your changes. Open your terminal and run the following command:
   git checkout -b my-new-branch
5. Make your changes. Make the changes to the code that you want to contribute.
6. Add your changes to Git. Open your terminal and run the following command:
   git add .
7. Commit your changes. Open your terminal and run the following command:
   git commit -m "My changes"
8. Push your changes to your forked repository. Open your terminal and run the following command:
   git push origin my-new-branch
9. Create a pull request. Go to the GitHub page for your forked repository and click the "Pull Request" button. This will create a pull request that proposes your changes to the maintainer of the ggerganovllamacpp repository.

Community Guidelines

When contributing to the ggerganovllamacpp project, please follow these community guidelines:

• Be respectful of other contributors.
• Follow the code style conventions of the project.
• Write clear and concise commit messages.
• Test your changes thoroughly before submitting a pull request.

Additional Resources

• ggerganovllamacpp GitHub repository
• ggerganovllamacpp contributing guidelines