Alpaca.cpp

Run a fast ChatGPT-like model locally on your device. The screencast below is not sped up and running on an M2 Macbook Air with 4GB of weights.

This combines the LLaMA foundation model with an open reproduction of Stanford Alpaca a fine-tuning of the base model to obey instructions (akin to the RLHF used to train ChatGPT) and a set of modifications to llama.cpp to add a chat interface.

Get started

git clone https://github.com/antimatter15/alpaca.cpp
cd alpaca.cpp

make chat
./chat

You can download the weights for ggml-alpaca-7b-q4.bin with BitTorrent magnet:?xt=urn:btih:5aaceaec63b03e51a98f04fd5c42320b2a033010&dn=ggml-alpaca-7b-q4.bin&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce&tr=udp%3A%2F%2Fopentracker.i2p.rocks%3A6969%2Fannounce

Alternatively you can download them with IPFS.

# any of these will work
wget -O ggml-alpaca-7b-q4.bin -c https://gateway.estuary.tech/gw/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
wget -O ggml-alpaca-7b-q4.bin -c https://ipfs.io/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
wget -O ggml-alpaca-7b-q4.bin -c https://cloudflare-ipfs.com/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC

Save the ggml-alpaca-7b-q4.bin file in the same directory as your ./chat executable.

The weights are based on the published fine-tunes from alpaca-lora, converted back into a pytorch checkpoint with a modified script and then quantized with llama.cpp the regular way.

Windows Setup

• Download and install CMake: https://cmake.org/download/
• Download and install git. If you've never used git before, consider a GUI client like https://desktop.github.com/
• Clone this repo using your git client of choice (for GitHub Desktop, go to File -> Clone repository -> From URL and paste https://github.com/antimatter15/alpaca.cpp in as the URL)
• Open a Windows Terminal inside the folder you cloned the repository to
• Run the following commands one by one:

cmake .
cmake --build . --config Release

• Download the weights via any of the links in "Get started" above, and save the file as ggml-alpaca-7b-q4.bin in the main Alpaca directory.
• In the terminal window, run this command:

.\Release\chat.exe

• (You can add other launch options like --n 8 as preferred onto the same line)
• You can now type to the AI in the terminal and it will reply. Enjoy!

13B

TODO

Torrent: magnet:?xt=urn:btih:f3cf71b172129d6b5abccab393bc32253fac8159&dn=ggml-alpaca-13b-q4.bin&tr=udp%3A%2F%http://2Ftracker.opentrackr.org%3A1337%2Fannounce&tr=udp%3A%2F%https://t.co/zenhelfwRd%3A6969%2Fannounce&tr=https%3A%2F%https://t.co/zenhelfwRd%3A443%2Fannounce&tr=udp%3A%2F%https://t.co/RRAn1X65wE%3A6969%2Fannounce&tr=udp%3A%2F%https://t.co/uTXBeTLUMa%3A2810%2Fannounce

Credit

This combines Facebook's LLaMA, Stanford Alpaca, alpaca-lora and corresponding weights by Eric Wang (which uses Jason Phang's implementation of LLaMA on top of Hugging Face Transformers), and llama.cpp by Georgi Gerganov. The chat implementation is based on Matvey Soloviev's Interactive Mode for llama.cpp. Inspired by Simon Willison's getting started guide for LLaMA.

Docker

Prerequisites

• Docker must be installed and running on your system.
• Create a folder to store big models & intermediate files (in ex. im using /llama/models)

Images

We have two Docker images available for this project:

1. ghcr.io/ggerganov/llama.cpp:full: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
2. ghcr.io/ggerganov/llama.cpp:light: This image only includes the main executable file.

Usage

The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.

docker run -v /llama/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B

On complete, you are ready to play!

docker run -v /llama/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512

or with light image:

docker run -v /llama/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512

Disclaimer

Note that the model weights are only to be used for research purposes, as they are derivative of LLaMA, and uses the published instruction data from the Stanford Alpaca project which is generated by OpenAI, which itself disallows the usage of its outputs to train competing models.