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Tensor library for machine learning

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# ggml

[Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205)

Tensor library for machine learning

***Note that this project is under active development. \
Some of the development is currently happening in the [llama.cpp](https://github.com/ggerganov/llama.cpp) and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) repos***

## Features

- Low-level cross-platform implementation
- Integer quantization support
- Broad hardware support
- Automatic differentiation
- ADAM and L-BFGS optimizers
- No third-party dependencies
- Zero memory allocations during runtime

## Build

```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
```

## GPT inference (example)

```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 information, checkout the corresponding programs in the [examples](examples) folder.

## Resources

- [Introduction to ggml](https://huggingface.co/blog/introduction-to-ggml)
- [The GGUF file format](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md)

ggml

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