Evalchemy
Evalchemy is a unified and easy-to-use toolkit designed for evaluating post-trained language models (LLMs). Developed by the DataComp community and Bespoke Labs, it builds on the LM-Eval-Harness to provide a comprehensive solution for model evaluation.
Key Features:
- Unified Installation: One-step setup for all benchmarks, eliminating dependency conflicts.
- Parallel Evaluation: Distribute evaluations across multiple GPUs for faster results.
- Simplified Usage: Run any benchmark with a consistent command-line interface.
- Results Management: Local results tracking with standardized output format and optional database integration for systematic tracking.
- Custom Evaluations: Implement custom evaluations and add external evaluation repositories easily.
- Support for Various Models: Compatibility with OpenAI models, vLLM models, and HuggingFace models.
Benefits:
- Efficiency: Dramatically reduce wall clock time for large benchmarks through parallel processing.
- Flexibility: Supports a wide range of benchmarks and model types, making it versatile for different evaluation needs.
- Cost-Effective: Provides insights into runtime and cost analysis, helping users optimize their evaluation processes.
Highlights:
- New reasoning benchmarks and model support are regularly added, enhancing the toolkit's capabilities.
- Detailed logging and debugging features to assist in troubleshooting and performance optimization.
Evalchemy makes running common benchmarks simple, fast, and versatile, making it an essential tool for researchers and developers working with LLMs.