Provide a summary of the following github project readme file, including the purpose of the project, what problems it may be used to solve, and anything the author mentions that differentiates this project from others:Title: Show HN: Alpaca.cpp – Run an Instruction-Tuned Chat-Style LLM on a MacBook Site: 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). Get started ``` git clone cd alpaca.cpp make chat ./chat ``` You can download the weights for ggml-alpaca-7b-14.bin with BitTorrent magnet:?xt=urn:btih:5aaceaec63b03e51a98f04fd5c42320b2a033010&dn=ggml-alpaca-7b-q4.bin& Alternatively you can download them with IPFS. ``` any of these will work wget -O ggml-alpaca-7b-q4.bin -c wget -O ggml-alpaca-7b-q4.bin -c wget -O ggml-alpaca-7b-q4.bin -c ``` Save the ggml-alpaca-7b-14.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. Credit This combines Facebook's LLaMA, Stanford Alpaca, alpaca-lora (which uses Jason Phang's implementation of LLaMA on top of Hugging Face Transformers), and a modified version of 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. 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.