llm-rs 0.2.2

Unofficial python bindings for llm-rs. 🐍❤️🦀

llm-rs-python: Python Bindings for Rust's llm Library

Welcome to llm-rs, an unofficial Python interface for the Rust-based llm library, made possible through PyO3. Our package combines the convenience of Python with the performance of Rust to offer an efficient tool for your machine learning projects. 🐍❤️🦀

With llm-rs, you can operate a variety of Large Language Models (LLMs) including LLama and GPT-NeoX directly on your CPU.

For a detailed overview of all the supported architectures, visit the llm project page.

Installation

Simply install it via pip: pip install llm-rs

Usage

Running GGML converted models:

This example shows how a Llama model can be loaded.

from llm_rs import Llama

#load the model
model = Llama("path/to/model.bin")

#generate
print(model.generate("The meaning of life is"))

Running Huggingface Hub Models

llm-rs supports automatic conversion of all supported transformer architectures on the Huggingface Hub.

To run covnersions additional dependencies are needed which can be installed via pip install llm-rs[convert].

The following example shows how a Pythia model can be covnverted, quantized and run.

from llm_rs.convert import AutoConverter
from llm_rs import AutoModel, AutoQuantizer
import sys

#define the model which should be converted and an output folder
export_folder = "path/to/folder" 
base_model = "EleutherAI/pythia-410m"

#convert the model
converted_model = AutoConverter.convert(base_model, export_folder)

#quantize the model (this step is optional)
quantized_model = AutoQuantizer.quantize(converted_model)

#load the quantized model
model = AutoModel.load(quantized_model,verbose=True)

#generate text
def callback(text):
    print(text,end="")
    sys.stdout.flush()

model.generate("The meaning of life is",callback=callback)

Documentation

For in-depth information on customizing the loading and generation processes, refer to our detailed documentation.