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LM EVALUATION HARNESS
The standard framework for LLM benchmarking
MIT
ABOUT
Evaluating LLMs requires running them against standardized benchmarks with consistent prompt formatting, task definitions, and scoring logic — doing this manually leads to irreproducible results and apples-to-oranges comparisons. The LM Evaluation Harness provides a centralized framework with hundreds of curated tasks (MMLU, GSM8K, HumanEval, etc.), automatic metric computation, and support for any model backend, making LLM evaluation reproducible, comparable, and scriptable.
INSTALL
pip install lm-evalINTEGRATION GUIDE
1. Benchmark a fine-tuned model against industry-standard tasks like MMLU and GSM8K to measure improvement over the base model
2. Compare multiple LLM providers (OpenAI, Anthropic, open-source) on the same set of tasks for procurement decisions
3. Run regression tests before deploying a new model version to detect accuracy regressions
4. Evaluate custom tasks designed around your specific domain or use case using the flexible task registration system
TAGS
pythonevaluationbenchmarkingllmaccuracytestingreproducibility