evaluating-llms-harness

You can benchmark LLMs on standardized academic tasks like MMLU and GSM8K by applying reproducible prompts and aggregating metrics. This Skill supports running evaluation suites across common tasks to generate consistent scores for model quality assessment.

You can evaluate API-backed models without needing a local HuggingFace runtime. This Skill supports HuggingFace, vLLM, and API-backed runtimes, allowing you to assess local models, distributed setups, and API-based workflows with batch sizing.

You track training progress by comparing model checkpoints during training to monitor improvement over time. This Skill provides consistent metrics across standardized tasks, enabling research teams to evaluate multiple versions and decide which checkpoint is ready for release.

Yes, this LLM evaluation harness supports custom task validation and optional multi-GPU execution. It allows you to assess distributed inference workflows and define custom tasks or task groups to validate specific model capabilities beyond standard benchmarks.

The best way to run reproducible LLM benchmarking across different model versions is to use a standardized evaluation suite with reproducible prompts and batch sizing. This ensures consistent metric aggregation for accurate comparison of model quality.