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-0.1.8.tar.gz (11.5 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-0.1.8-py3-none-any.whl (10.6 kB view details)

Uploaded Python 3

File details

Details for the file ai_launcher_cli-0.1.8.tar.gz.

File metadata

  • Download URL: ai_launcher_cli-0.1.8.tar.gz
  • Upload date:
  • Size: 11.5 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-0.1.8.tar.gz
Algorithm Hash digest
SHA256 91f778c149b419aa3184a20707076586f3a192c1725a0445896bf3c475ce1aea
MD5 1db2e0b82bdea8a6f25873db398a4d94
BLAKE2b-256 7097dff91fc52a740a02ed53b7491e5cb791beee49e2b85f207e72656b23e557

See more details on using hashes here.

File details

Details for the file ai_launcher_cli-0.1.8-py3-none-any.whl.

File metadata

File hashes

Hashes for ai_launcher_cli-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 b8e4dfab005d27286af10f5d4d6cb1b9908f7cb48e922d97221c93efff1d1eee
MD5 63e68d9dae685b02d35c27aa60a0bcbc
BLAKE2b-256 df7f81cad2841a796950bcef9800f2cf30af6ac2a4728a5d64a2b71a40161e49

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