This tool automates the optimization of prompts for Llama models, targeting developers and researchers who need to improve LLM performance without manual trial-and-error. It transforms existing prompts for other LLMs into Llama-optimized versions, offering faster, data-driven improvements and measurable results.

How It Works

The tool employs a template-based optimization approach, taking an existing system prompt, a query-response dataset, and a YAML configuration file. It then uses the llama-prompt-ops migrate command to process these inputs, generating an optimized prompt and performance metrics. This method aims to reduce manual prompt engineering effort and deliver quantifiable performance gains.

Quick Start & Requirements

• Installation: pip install llama-prompt-ops or install from source.
• Prerequisites: Python 3.10, an existing system prompt, a JSON file with at least 50 query-response pairs, and a YAML configuration file. An OpenRouter API key is required for the default setup.
• Setup Time: Approximately 5 minutes.
• Documentation: Quick Start Guide, Basic Tutorial, Inference Providers Guide.

Highlighted Details

• Demonstrates substantial improvements on the HotpotQA benchmark across various model sizes.
• Supports multiple inference providers including OpenRouter, vLLM, and NVIDIA NIMs.
• Allows for custom data formats via DatasetAdapter extension.
• Leverages DSPy for its underlying framework.

Maintenance & Community

• The project is maintained by meta-llama.
• Contributions are welcome via Pull Requests.

Licensing & Compatibility

• Licensed under the MIT License.
• Permissive license suitable for commercial use and integration with closed-source applications.

Limitations & Caveats

The tool requires a specific JSON format for datasets or custom adapter implementation for other formats. While it supports multiple inference providers, initial setup relies on an OpenRouter API key.