evaluating-llms-harness

Benchmark LLMs across standard academic tasks by running scripted evaluation pipelines with 5-shot configurations. This framework quantifies model capabilities across 60+ benchmarks including MMLU, GSM8K, HumanEval, TruthfulQA, and HellaSwag to generate objective performance results.

Evaluating large language models for reproducibility requires standardized prompts and scripted evaluation pipelines. This framework supports HuggingFace and vLLM backends to ensure consistent benchmarking results across different runs for model development and paper benchmarking.

Yes, LLM evaluation supports popular backends including HuggingFace, vLLM, and local API endpoints. This allows flexible deployment for benchmarking models across diverse tasks while maintaining standardized evaluation metrics and reproducible pipelines.

Run a quick start benchmark by installing the lm-evaluation-harness and executing a standard benchmark on your model with a 5-shot configuration. This generates evaluation results quantifying performance across knowledge, reasoning, and code generation tasks.

Standard LLM benchmarks cover 60+ academic tasks including MMLU for knowledge, GSM8K for reasoning, HumanEval for code generation, TruthfulQA for factual accuracy, and HellaSwag for commonsense reasoning, enabling comprehensive model selection and progress monitoring.