llm - Large Language Models for Everyone, in Rust

llm is an ecosystem of Rust libraries for working with large language models - it's built on top of the fast, efficient GGML library for machine learning.

Image by @darthdeus, using Stable Diffusion

The primary entrypoint for developers is the llm crate, which wraps llm-base and the supported model crates. Documentation for released version is available on Docs.rs.

For end-users, there is a CLI application, llm-cli, which provides a convenient interface for interacting with supported models. Text generation can be done as a one-off based on a prompt, or interactively, through REPL or chat modes. The CLI can also be used to serialize (print) decoded models, quantize GGML files, or compute the perplexity of a model. It can be downloaded from the latest GitHub release or by installing it from crates.io.

llm is powered by the ggml tensor library, and aims to bring the robustness and ease of use of Rust to the world of large language models. At present, inference is only on the CPU, but we hope to support GPU inference in the future through alternate backends.

Currently, the following models are supported:

• BLOOM
• GPT-2
• GPT-J
• GPT-NeoX (includes StableLM, RedPajama, and Dolly 2.0)
• LLaMA (includes Alpaca, Vicuna, Koala, GPT4All, and Wizard)
• MPT

Using llm in a Rust Project

This project depends on Rust v1.65.0 or above and a modern C toolchain.

The llm crate exports llm-base and the model crates (e.g. bloom, gpt2 llama).

Add llm to your project by listing it as a dependency in Cargo.toml. To use the version of llm you see in the main branch of this repository, add it from GitHub (although keep in mind this is pre-release software):

[dependencies]
llm = { git = "https://github.com/rustformers/llm" , branch = "main" }

To use a released version, add it from crates.io by specifying the desired version:

[dependencies]
llm = "0.1"

NOTE: To improve debug performance, exclude the transitive ggml-sys dependency from being built in debug mode:

[profile.dev.package.ggml-sys]
opt-level = 3

Leverage Accelerators with llm

The llm library is engineered to take advantage of hardware accelerators such as cuda and metal for optimized performance.

To enable llm to harness these accelerators, some preliminary configuration steps are necessary, which vary based on your operating system. For comprehensive guidance, please refer to the Acceleration Support for Building section in our documentation.

Using llm from Other Languages

Bindings for this library are available in the following languages:

• Python: LLukas22/llm-rs-python
• Node: Atome-FE/llama-node

Using the llm CLI

The easiest way to get started with llm-cli is to download a pre-built executable from a released version of llm, although this may not have all the features present on the main branch. The following methods involve building llm, which requires Rust v1.65.0 or above and a modern C toolchain.

Installing with cargo

To install the most recently released version of llm to your Cargo bin directory, which rustup is likely to have added to your PATH, run:

cargo install llm-cli

The CLI application can then be run through llm.

Building from Source

To make use of the features on the main branch, clone the repository and then build it with

git clone --recurse-submodules git@github.com:rustformers/llm.git
cargo build --release

The resulting binary will be at target/release/llm[.exe].

It can also be run directly through Cargo, with

cargo run --release -- $ARGS

Getting Models

GGML files are easy to acquire. For a list of models that have been tested, see the known-good models.

Certain older GGML formats are not supported by this project, but the goal is to maintain feature parity with the upstream GGML project. For problems relating to loading models, or requesting support for supported GGML model types, please open an Issue.

From Hugging Face

Hugging Face 🤗 is a leader in open-source machine learning and hosts hundreds of GGML models. Search for GGML models on Hugging Face 🤗.

r/LocalLLaMA

This Reddit community maintains a wiki related to GGML models, including well organized lists of links for acquiring GGML models (mostly from Hugging Face 🤗).

Usage

Once the llm executable has been built or is in a $PATH directory, try running it. Here's an example that uses the open-source RedPajama language model:

llm gptneox infer -m RedPajama-INCITE-Base-3B-v1-q4_0.bin -p "Rust is a cool programming language because" -r togethercomputer/RedPajama-INCITE-Base-3B-v1

In the example above, the first two arguments specify the model architecture and command, respectively. The required -m argument specifies the local path to the model, and the required -p argument specifies the evaluation prompt. The optional -r argument is used to load the model's vocabulary from a remote Hugging Face 🤗 repository, which will typically improve results when compared to loading the vocabulary from the model file itself; there is also an optional -v argument that can be used to specify the path to a local vocabulary file. For more information about the llm CLI, use the --help parameter.

There is also a simple inference example that is helpful for debugging:

cargo run --release --example inference gptneox RedPajama-INCITE-Base-3B-v1-q4_0.bin -r $OPTIONAL_VOCAB_REPO -p $OPTIONAL_PROMPT

Q&A

Does the llm CLI support chat mode?

Yes, but certain fine-tuned models (e.g. Alpaca, Vicuna, Pygmalion) are more suited to chat use-cases than so-called "base models". Here's an example of using the llm CLI in REPL (Read-Evaluate-Print Loop) mode with an Alpaca model - note that the provided prompt format is tailored to the model that is being used:

llm llama repl -m ggml-alpaca-7b-q4.bin -f utils/prompts/alpaca.txt

There is also a Vicuna chat example that demonstrates how to create a custom chatbot:

cargo run --release --example vicuna-chat llama ggml-vicuna-7b-q4.bin

Can llm sessions be persisted for later use?

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.

How do I use llm to quantize a model?

llm can produce a q4_0- or q4_1-quantized model from an f16-quantized GGML model

cargo run --release $MODEL_ARCHITECTURE quantize $MODEL_IN $MODEL_OUT {q4_0,q4_1}

Do you provide support for Docker and NixOS?

The llm Dockerfile is in the utils directory; the NixOS flake manifest and lockfile are in the project root.

What's the best way to get in touch with the llm community?

GitHub Issues and Discussions are welcome, or come chat on Discord!

Do you accept contributions?

Absolutely! Please see the contributing guide.

What applications and libraries use llm?

Applications

• llmcord: Discord bot for generating messages using llm.
• local.ai: Desktop app for hosting an inference API on your local machine using llm.
• secondbrain: Desktop app to download and run LLMs locally in your computer using llm.

Libraries

• llm-chain: Build chains in large language models for text summarization and completion of more complex tasks