Skip to main content

Simple CLI to chat with GGUF models locally (no Ollama/LM Studio required)

Project description

ai-launcher-cli

Simple Python CLI to chat with .gguf models locally using llama-cpp-python. No Ollama, llama.cpp, LM Studio, or online providers required.

Install

pip install ai-launcher-cli

Usage

# Basic usage
ailaunch path/to/model.gguf

# With custom settings
ailaunch model.gguf -c 8192 -t 0.8 --max-tokens 1024

# Disable streaming (wait for full response)
ailaunch model.gguf --no-stream

# Custom system prompt
ailaunch model.gguf --system "You are a coding assistant."

# Use a built-in system prompt template
ailaunch model.gguf --system-template coder

# List available models
ailaunch --list-models

# Auto-select model from common directories
ailaunch auto

# Options:
#   -c, --ctx-size      Context window size (default: 4096)
#   -g, --gpu-layers    GPU layers to offload (-1 = all, default: -1)
#   -t, --threads       CPU threads (0 = auto, default: 0)
#   --temperature        Sampling temperature (default: 0.7)
#   --max-tokens        Max tokens to generate (default: 512)
#   --no-stream         Disable streaming output
#   --system            Custom system prompt
#   --system-template   Built-in template (coder, reviewer, teacher, creative, analyst, translator, shell)
#   --list-models       List available GGUF models and exit
#   --save-config       Save current options as defaults
#   --benchmark         Run benchmark after loading
#   --export            Export conversation on exit (markdown/json)
#   --export-file       File to export conversation to
#   --no-history        Disable loading/saving chat history
#   --clear-history     Clear chat history for this model
#   -v, --version       Show version

Interactive Commands

While chatting, type any of these commands:

Command Description
/help Show help
/save Save conversation to history
/export [fmt] Export conversation (markdown/json)
/clear Clear conversation (keep system prompt)
/system <prompt> Change system prompt
/template <name> Use built-in template
/config Show current configuration
/bench Run benchmark
/models List available models
/switch [path] Switch to another model
exit/quit/q Exit

Configuration

Config is saved to ~/.config/ailaunch/config.yaml. Use --save-config to save current options.

Model Auto-Detection

Models are automatically searched in these directories:

  • ~/.lmstudio/models
  • ~/.lmstudio/.internal/bundled-models
  • ~/.cache/huggingface/hub
  • ~/models
  • ~/Downloads
  • ~/OneDrive/Downloads
  • ~/OneDrive/Documents/Downloads

GPU Acceleration

Install with GPU extras for acceleration:

# NVIDIA CUDA
pip install ai-launcher-cli[cuda]

# Apple Metal
pip install ai-launcher-cli[metal]

Then use -g -1 to offload all layers to GPU.

Requirements

  • Python 3.8+
  • llama-cpp-python>=0.3.0 (installs automatically)

Exit

Type exit, quit, q or press Ctrl+C to exit.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ai_launcher_cli-0.1.5.tar.gz (9.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ai_launcher_cli-0.1.5-py3-none-any.whl (9.1 kB view details)

Uploaded Python 3

File details

Details for the file ai_launcher_cli-0.1.5.tar.gz.

File metadata

  • Download URL: ai_launcher_cli-0.1.5.tar.gz
  • Upload date:
  • Size: 9.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.13

File hashes

Hashes for ai_launcher_cli-0.1.5.tar.gz
Algorithm Hash digest
SHA256 b193d437786b9b2842c694e0c6bd5a1c2f86e45332c0f84fef637fd145205617
MD5 26fa9d3093d7e8a36ceacecfc0fc0162
BLAKE2b-256 564b6f830805a412e70f7df709f79dcae12a9230a7c88842362def84669b8cae

See more details on using hashes here.

File details

Details for the file ai_launcher_cli-0.1.5-py3-none-any.whl.

File metadata

File hashes

Hashes for ai_launcher_cli-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 bdb0dddc903c1a6557e3dd31e630babbe7e5c27b2c0edb7ed7cc3756e64195a8
MD5 e89f9c409807f7747a7bad786ba5584c
BLAKE2b-256 9d6269121007e2fa0b9b17e2c8bbbd296c7dca7eab26c26dcb2162b64fc6b907

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page