Safe, high-level Rust bindings to the C++ project of the same name, meant to be as user-friendly as possible. Run GGUF-based large language models directly on your CPU in fifteen lines of code, no ML experience required!
// Create a model from anything that implements `AsRef<Path>`:
let model = LlamaModel::load_from_file("path_to_model.gguf", LlamaParams::default()).expect("Could not load model");
// A `LlamaModel` holds the weights shared across many _sessions_; while your model may be
// several gigabytes large, a session is typically a few dozen to a hundred megabytes!
let mut ctx = model.create_session(SessionParams::default()).expect("Failed to create session");
// You can feed anything that implements `AsRef<[u8]>` into the model's context.
ctx.advance_context("This is the story of a man named Stanley.").unwrap();
// LLMs are typically used to predict the next word in a sequence. Let's generate some tokens!
let max_tokens = 1024;
let mut decoded_tokens = 0;
// `ctx.start_completing_with` creates a worker thread that generates tokens. When the completion
// handle is dropped, tokens stop generating!
let mut completions = ctx.start_completing_with(StandardSampler::default(), 1024).into_strings();
for completion in completions {
print!("{completion}");
let _ = io::stdout().flush();
decoded_tokens += 1;
if decoded_tokens > max_tokens {
break;
}
}This repository hosts the high-level bindings (crates/llama_cpp) as well as automatically generated bindings to
llama.cpp's low-level C API (crates/llama_cpp_sys). Contributions are welcome--just keep the UX clean!
Keep in mind that llama.cpp is very computationally heavy, meaning standard
debug builds (running just cargo build/cargo run) will suffer greatly from the lack of optimisations. Therefore, unless
debugging is really necessary, it is highly recommended to build and run using Cargo's --release flag.
MIT or Apache-2.0, at your option (the "Rust" license). See LICENSE-MIT and LICENSE-APACHE.