# ggml: A Low-Level Tensor Library for Machine Learning

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ggml is an innovative tensor library designed for machine learning, emphasizing low-level, cross-platform implementation. It offers features like integer quantization, automatic differentiation, and broad hardware support, all while maintaining zero third-party dependencies and efficient memory usage. This project is actively developed and forms the backbone for other popular projects like llama.cpp and whisper.cpp.

GitHub: https://github.com/ggml-org/ggml
OSRepos URL: https://osrepos.com/repo/ggml-org-ggml

## Summary

ggml is an innovative tensor library designed for machine learning, emphasizing low-level, cross-platform implementation. It offers features like integer quantization, automatic differentiation, and broad hardware support, all while maintaining zero third-party dependencies and efficient memory usage. This project is actively developed and forms the backbone for other popular projects like llama.cpp and whisper.cpp.

## Topics

- machine-learning
- tensor-library
- large-language-models
- automatic-differentiation
- C++
- AI
- deep-learning
- quantization

## Repository Information

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## Content

## Introduction

ggml is a powerful tensor library specifically engineered for machine learning applications. It stands out for its low-level, cross-platform implementation, making it highly versatile and efficient. The library is under active development, with significant contributions also happening within related projects such as [llama.cpp](https://github.com/ggerganov/llama.cpp "llama.cpp" target="_blank") and [whisper.cpp](https://github.com/ggerganov/whisper.cpp "whisper.cpp" target="_blank").

Key features of ggml include integer quantization support, broad hardware compatibility, and built-in automatic differentiation. It also incorporates ADAM and L-BFGS optimizers, all without relying on any third-party dependencies, ensuring a lean and performant codebase. A notable design principle is its commitment to zero memory allocations during runtime, which contributes to its exceptional efficiency.

## Installation

To get started with ggml, clone the repository and follow the build steps:

bash
git clone https://github.com/ggml-org/ggml
cd ggml

# install python dependencies in a virtual environment
python3.10 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# build the examples
mkdir build && cd build
cmake ..
cmake --build . --config Release -j 8


## Examples

Here's an example of how to run GPT-2 inference using ggml:

bash
# run the GPT-2 small 117M model
../examples/gpt-2/download-ggml-model.sh 117M
./bin/gpt-2-backend -m models/gpt-2-117M/ggml-model.bin -p "This is an example"


For more detailed examples and usage scenarios, explore the `examples` folder within the repository.

## Why Use ggml?

ggml offers several compelling advantages for machine learning developers:

*   **Low-level and Cross-platform:** Provides fine-grained control and runs efficiently across various operating systems and architectures.
*   **Integer Quantization Support:** Enables efficient model deployment on resource-constrained devices by reducing model size and computational requirements.
*   **Broad Hardware Support:** Designed to work across a wide range of hardware, including specialized accelerators.
*   **Automatic Differentiation:** Simplifies the implementation of complex machine learning models and training algorithms.
*   **Optimizers Included:** Comes with ADAM and L-BFGS optimizers built-in, ready for use in training processes.
*   **No Third-Party Dependencies:** Reduces complexity, potential conflicts, and improves portability.
*   **Zero Memory Allocations During Runtime:** Ensures highly efficient memory usage, crucial for performance-critical applications.

## Links

*   **GitHub Repository:** [https://github.com/ggml-org/ggml](https://github.com/ggml-org/ggml "ggml GitHub Repository" target="_blank")
*   **Roadmap:** [https://github.com/users/ggerganov/projects/7](https://github.com/users/ggerganov/projects/7 "ggml Roadmap" target="_blank")
*   **Manifesto:** [https://github.com/ggerganov/llama.cpp/discussions/205](https://github.com/ggerganov/llama.cpp/discussions/205 "ggml Manifesto" target="_blank")
*   **Introduction to ggml (Hugging Face Blog):** [https://huggingface.co/blog/introduction-to-ggml](https://huggingface.co/blog/introduction-to-ggml "Introduction to ggml" target="_blank")
*   **The GGUF file format:** [https://github.com/ggerganov/ggml/blob/master/docs/gguf.md](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md "GGUF file format documentation" target="_blank")