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.
Several of llama.cpp's backends are supported through features:
cuda- Enables the CUDA backend, the CUDA Toolkit is required for compilation if this feature is enabled.vulkan- Enables the Vulkan backend, the Vulkan SDK is required for compilation if this feature is enabled.metal- Enables the Metal backend, macOS only.hipblas- Enables the hipBLAS/ROCm backend, ROCm is required for compilation if this feature is enabled.
Something that's provided by these bindings is the ability to predict context size in memory, however it should be noted that this is a highly experimental feature as this isn't something that llama.cpp itself provides. The returned values may be highly inaccurate, however an attempt is made to never return values lower than the real size.
MIT or Apache-2.0, at your option (the "Rust" license). See LICENSE-MIT and LICENSE-APACHE.