zoom_in Click to enlarge

llama.cpp-python

▲ ▼

language

description llama.cpp-python Overview

This Python binding allows developers to interact with the highly optimized llama.cpp engine directly within Python scripts. This is invaluable for creating custom, automated workflowsfor instance, writing a script that reads a file, sends it to the local LLM via this library, and then parses the structured JSON output. It offers maximum programmatic control.

help llama.cpp-python FAQ

How do I install llama-cpp-python and load a model?

Run 'pip install llama-cpp-python' for the prebuilt CPU package, then load a GGUF file with 'Llama.from_pretrained()' or by pointing Llama(model_path=...) at a local file. GPU acceleration requires rebuilding with the appropriate CMAKE flags rather than the default PyPI wheel.

Does llama-cpp-python support GPU inference on my hardware?

Yes. The binding supports NVIDIA CUDA, Apple Metal, AMD ROCm, and Vulkan backends, but the hardware-enabled builds aren't the default on PyPI. You set CMAKE_ARGS environment variables before installing to compile against your GPU toolkit.

Can it expose an OpenAI-compatible API endpoint?

Yes. Running 'python -m llama_cpp.server' spins up an OpenAI-style REST API, and the library's chat and completion interfaces are designed so OpenAI client code can be pointed at your local model with minimal changes. This is what makes scripted workflows drop-in easy.

What model file format does it expect?

It runs GGUF files, the quantized model format used across the llama.cpp ecosystem and widely mirrored on Hugging Face. Mid-tier quantizations like Q4_K_M are the usual sweet spot for running 7B–13B models on modest RAM.

swap_horiz

Looking for llama.cpp-python alternatives? Compare top competitors ranked & scored

arrow_forward