LLaMA-rs

Do the LLaMA thing, but now in Rust 🦀🚀🦙

Image by @darthdeus, using Stable Diffusion

LLaMA-rs is a Rust port of the llama.cpp project. This allows running inference for Facebook's LLaMA model on a CPU with good performance using full precision, f16 or 4-bit quantized versions of the model.

Just like its C++ counterpart, it is powered by the ggml tensor library, achieving the same performance as the original code.

Getting started

Make sure you have a Rust 1.65.0 or above and C toolchain¹ set up.

llama-rs is a Rust library, while llama-cli is a CLI application that wraps llama-rs and offers basic inference capabilities.

The following instructions explain how to build llama-cli.

NOTE: For best results, make sure to build and run in release mode. Debug builds are going to be very slow.

Building using `cargo`

Run

cargo install --git https://github.com/rustformers/llama-rs llama-cli

to install llama-cli to your Cargo bin directory, which rustup is likely to have added to your PATH.

It can then be run through llama-cli.

Building from repository

Clone the repository, and then build it through

cargo build --release --bin llama-cli

The resulting binary will be at target/release/llama-cli[.exe].

It can also be run directly through Cargo, using

cargo run --release --bin llama-cli -- <ARGS>

This is useful for development.

Getting the weights

In order to run the inference code in llama-rs, a copy of the model's weights are required. Currently, the only legal source to get the weights is this repository. Note that the choice of words also may or may not hint at the existence of other kinds of sources.

After acquiring the weights, it is necessary to convert them into a format that is compatible with ggml. To achieve this, follow the steps outlined below:

Warning

To run the Python scripts, a Python version of 3.9 or 3.10 is required. 3.11 is unsupported at the time of writing.

# Convert the model to f16 ggml format
python3 scripts/convert-pth-to-ggml.py /path/to/your/models/7B/ 1

# Quantize the model
**Note** To quantize the model, for now using llama.cpp is necessary. This will be fixed once #84 is merged.

Note

The llama.cpp repository has additional information on how to obtain and run specific models. With some caveats:

Currently, llama-rs supports both the old (unversioned) and the new (versioned) ggml formats, but not the mmap-ready version that was recently merged.

Support for other open source models is currently planned. For models where weights can be legally distributed, this section will be updated with scripts to make the install process as user-friendly as possible. Due to the model's legal requirements, this is currently not possible with LLaMA itself and a more lengthy setup is required.

Running

For example, try the following prompt:

llama-cli infer -m <path>/ggml-model-q4_0.bin -p "Tell me how cool the Rust programming language is:"

Some additional things to try:

Use --help to see a list of available options.

If you have the alpaca-lora weights, try repl mode!

llama-cli repl -m <path>/ggml-alpaca-7b-q4.bin -f examples/alpaca_prompt.txt

Sessions can be loaded (--load-session) or saved (--save-session) to file. To automatically load and save the same session, use --persist-session. This can be used to cache prompts to reduce load time, too:

(This GIF shows an older version of the flags, but the mechanics are still the same.)

Docker

# To build (This will take some time, go grab some coffee):
docker build -t llama-rs .

# To run with prompt:
docker run --rm --name llama-rs -it -v ${PWD}/data:/data -v ${PWD}/examples:/examples llama-rs infer -m data/gpt4all-lora-quantized-ggml.bin -p "Tell me how cool the Rust programming language is:"

# To run with prompt file and repl (will wait for user input):
docker run --rm --name llama-rs -it -v ${PWD}/data:/data -v ${PWD}/examples:/examples llama-rs repl -m data/gpt4all-lora-quantized-ggml.bin -f examples/alpaca_prompt.txt

Q&A

Why did you do this?

It was not my choice. Ferris appeared to me in my dreams and asked me to rewrite this in the name of the Holy crab.

Seriously now.

Come on! I don't want to get into a flame war. You know how it goes, something something memory something something cargo is nice, don't make me say it, everybody knows this already.

I insist.

Sheesh! Okaaay. After seeing the huge potential for llama.cpp, the first thing I did was to see how hard would it be to turn it into a library to embed in my projects. I started digging into the code, and realized the heavy lifting is done by ggml (a C library, easy to bind to Rust) and the whole project was just around ~2k lines of C++ code (not so easy to bind). After a couple of (failed) attempts to build an HTTP server into the tool, I realized I'd be much more productive if I just ported the code to Rust, where I'm more comfortable.

Is this the real reason?

Haha. Of course not. I just like collecting imaginary internet points, in the form of little stars, that people seem to give to me whenever I embark on pointless quests for rewriting X thing, but in Rust.

How is this different from `llama.cpp`?

This is a reimplementation of llama.cpp that does not share any code with it outside of ggml. This was done for a variety of reasons:

llama.cpp requires a C++ compiler, which can cause problems for cross-compilation to more esoteric platforms. An example of such a platform is WebAssembly, which can require a non-standard compiler SDK.
Rust is easier to work with from a development and open-source perspective; it offers better tooling for writing "code in the large" with many other authors. Additionally, we can benefit from the larger Rust ecosystem with ease.
We would like to make ggml an optional backend (see this issue).

In general, we hope to build a solution for model inferencing that is as easy to use and deploy as any other Rust crate.

A modern-ish C toolchain is required to compile ggml. A C++ toolchain should not be necessary. ↩

drbh/llama-rs