evaluating-llms-harness

You can benchmark LLMs across 60+ academic benchmarks like MMLU and GSM8K to quantify model performance, using pluggable backends to drive the evaluations and generate reproducible results.

Yes, you can run LLM evaluation using pluggable HuggingFace, vLLM, and API backends, allowing flexible deployment across local model weights or remote API endpoints for benchmarking tasks.

The best way to track LLM training progress over time is by running standardized benchmark evaluations to generate reproducible results, enabling consistent model comparisons and academic reporting across checkpoints.

Available academic benchmarks for evaluating LLM model quality include 60+ evaluation tasks such as MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag, and ARC, providing comprehensive coverage of model capabilities.

You need a YAML frontmatter in SKILL.md with at least a name and description to configure the evaluation harness, then install lm-eval and run a baseline evaluation against your model to generate initial results.