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
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 = 3The 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.
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-cliThe CLI application can then be run through llm.
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 --releaseThe resulting binary will be at target/release/llm[.exe].
It can also be run directly through Cargo, with
cargo run --release -- $ARGSGGML 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.
Hugging Face 🤗 is a leader in open-source machine learning and hosts hundreds of GGML models. Search for GGML models on Hugging Face 🤗.
This Reddit community maintains a wiki related to GGML models, including well organized lists of links for acquiring GGML models (mostly from Hugging Face 🤗).
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-v1In 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_PROMPTYes, 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.txtThere 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.binSessions 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.
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}The llm Dockerfile is in the utils directory, as is a
NixOS flake manifest and lockfile.
GitHub Issues and Discussions are welcome, or come chat on Discord!
Absolutely! Please see the contributing guide.
- llmcord: Discord bot for generating
messages using
llm. - local.ai: Desktop app for hosting an
inference API on your local machine using
llm.
- llm-chain: Build chains in large language models for text summarization and completion of more complex tasks