lmstrix 1.0.50

A toolkit for managing and testing LM Studio models with automatic context limit discovery

LMStrix is a professional, installable Python toolkit designed to supercharge your interaction with LM Studio. It provides a powerful command-line interface (CLI) and a clean Python API for managing, testing, and running local language models, with a standout feature: the Adaptive Context Optimizer.

For the full documentation, please visit the LMStrix GitHub Pages site.

Key Features

• Automatic Context Optimization: Discover the true context limit of any model with the test command.
• Full Model Management: Programmatically list available models and scan for newly downloaded ones.
• Flexible Inference Engine: Run inference with a powerful two-phase prompt templating system that separates prompt structure from its content.
• Rich CLI: A beautiful and intuitive command-line interface built with rich and fire.
• Modern Python API: An async-first API designed for high-performance, concurrent applications.

Installation

# Using pip
pip install lmstrix

# Using uv (recommended)
uv pip install lmstrix

For more detailed installation instructions, see the Installation page.

Quick Start

Command-Line Interface (CLI)

# First, scan for available models in LM Studio
lmstrix scan

# List all models with their test status
lmstrix list

# Test the context limit for a specific model
lmstrix test "model-id-here"

# Test all untested models with enhanced safety controls
lmstrix test --all --threshold 102400

# Test all models at a specific context size
lmstrix test --all --ctx 32768

# Sort and filter model listings
lmstrix list --sort dtx  # Sort by declared context size descending
lmstrix list --show json --sort size  # Export as JSON sorted by model size

# Run inference on a model
lmstrix infer "Your prompt here" --model "model-id" --max-tokens 150

Python API

from lmstrix import LMStrix

def main():
    # Initialize the client
    lms = LMStrix()
    
    # Scan for available models
    lms.scan_models()
    
    # List all models
    models = lms.list_models()
    print(models)
    
    # Test a specific model's context limits
    model_id = models[0].id if models else None
    if model_id:
        result = lms.test_model(model_id)
        print(result)
    
    # Run inference
    if model_id:
        response = lms.infer(
            prompt="What is the meaning of life?",
            model_id=model_id,
            max_tokens=100
        )
        print(response.content)

if __name__ == "__main__":
    main()

For more detailed usage instructions and examples, see the Usage page and the API Reference.

Enhanced Testing Strategy

LMStrix uses a sophisticated testing algorithm to safely and efficiently discover true model context limits:

Safety Features

• Threshold Protection: Default 102,400 token limit prevents system crashes from oversized contexts
• Smart Validation: Checks against previously known bad context sizes to avoid repeated failures
• Progressive Testing: Incremental approach minimizes resource usage while maximizing accuracy

Testing Algorithm

1. Initial Verification: Tests at small context (1024) to verify model loads
2. Threshold Test: Tests at min(threshold, declared_limit) for safe initial assessment
3. Incremental Search: If threshold succeeds, incrementally increases by 10,240 tokens
4. Binary Search: On failure, performs efficient binary search to find exact limit
5. Progress Persistence: Saves results after each test for resumable operations

Multi-Model Optimization

• Batch Processing: --all flag efficiently tests multiple models with minimal loading/unloading
• Smart Sorting: Tests models in optimal order to reduce resource cycling
• Flexible Filtering: Target specific context sizes or model subsets
• Rich Output: Beautiful tables showing results, efficiency, and progress

Development

# Clone the repository
git clone https://github.com/twardoch/lmstrix
cd lmstrix

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

# Run the test suite
pytest

Changelog

All notable changes to this project are documented in the CHANGELOG.md file.

License

This project is licensed under the MIT License - see the LICENSE file for details.