llamacpp-cli 0.1.5

Ollama-like CLI wrapper around llama.cpp

llamacpp-cli

Ollama-like CLI wrapper around llama.cpp. Provides a simple command-line interface that mirrors Ollama's subcommands but powered by llama.cpp as the backend inference engine.

Features

• pull - Download GGUF models from Hugging Face
• run - Run models interactively using llama.cpp
• serve - Start the llama.cpp server
• lb-proxy - Multi-backend load balancer proxy (NEW!)
• list - List downloaded models
• ps - Show running llama.cpp processes
• rm - Remove a downloaded model
• search - Search Hugging Face for GGUF models
• install - Install/update llama.cpp binaries

Installation

From PyPI

pip install llamacpp-cli

From Source

pip install -e .

Quick Start

1. Install llama.cpp binaries

llamacpp install

This downloads the latest llama.cpp release to ~/.llamacpp/bin/.

2. Pull a model

llamacpp pull unsloth/gemma-3-270m-it-GGUF:Q4_K_M

Or use a short alias:

llamacpp pull gemma3:270m

3. Run interactively

llamacpp run gemma3:270m

4. Start the server

llamacpp serve -m gemma3:270m

The server runs at http://localhost:8080 with OpenAI-compatible API.

Commands

llamacpp pull <model>      Download GGUF model from Hugging Face
llamacpp run <model>       Run a model interactively
llamacpp serve             Start the llama.cpp server
llamacpp lb-proxy          Start multi-backend load balancer (see LB_PROXY.md)
llamacpp list              List downloaded models
llamacpp ps                Show running processes
llamacpp rm <model>        Remove a model
llamacpp search <query>    Search for models on Hugging Face
llamacpp install           Install/update llama.cpp binaries

Load Balancer Proxy

For distributing requests across multiple machines, use the load balancer:

# Auto-discover backends on your network
llamacpp lb-proxy --discover-subnet 192.168.1.0/24

# Or specify backends manually
llamacpp lb-proxy -b http://machine1:8000 -b http://machine2:8000

See LB_PROXY.md for detailed documentation on:

• Model-aware routing
• Least-connections load balancing
• Auto-discovery and health checks
• Configuration options

Model Names

Model names can be specified in multiple ways:

• Full Hugging Face path: unsloth/gemma-3-270m-it-GGUF:Q4_K_M
• Short format: namespace/model:quantization (e.g., gemma3:270m)
• Short name: gemma3:270m, qwen3, llama3:8b

Alias support is planned for future releases.

Configuration

• Models are stored in ~/.llamacpp/models/
• Binaries are installed to ~/.llamacpp/bin/
• Database (SQLite) is at ~/.llamacpp/llamacpp.db

Environment Variables

Variable	Description	Default
LLAMACPP_BIN_DIR	Directory for llama.cpp binaries	~/.llamacpp/bin
LLAMACPP_MODEL_DIR	Directory for models	~/.llamacpp/models

Usage with LLM CLI

This package also registers as an LLM plugin for the llm CLI:

# Install the plugin (requires llm and llama-cpp-python)
pip install llm-llama-cpp llama-cpp-python

# Register a model
llm llama-cpp add-model ~/.llamacpp/models/gemma-3-270m-it-Q4_K_M.gguf --alias gemma3:270m

# Use with llm
llm -m gemma3:270m "Your prompt here"

Development

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run a single test file
pytest tests/test_foo.py

# Lint
ruff check .

# Format
ruff format .

Publishing to PyPI

Prerequisites

1. Create a PyPI account at https://pypi.org/
2. Install build tools:

pip install build twine

Build and Publish

1. Update version in pyproject.toml:

[project]
version = "0.1.0"

2. Build the package:

python -m build

This creates distributable archives in dist/.

3. Upload to PyPI:

twine upload dist/*

You'll be prompted for your PyPI username and password.

For Test PyPI (testing first):

twine upload --repository testpypi dist/*

Using uv (Alternative)

# Install uv if not already
pip install uv

# Build
uv build

# Publish to PyPI
uv publish

# Or Test PyPI
uv publish --test

License

MIT