evaluating-llms-harness

To benchmark LLMs against MMLU and GSM8K, use a unified evaluation harness that configures prompts, few-shot settings, and metrics to run reproducible comparisons. It orchestrates task loading and outputs structured JSON or CSV results for downstream analytics.

Yes, you can evaluate local models and OpenAI-compatible endpoints together. The evaluation harness supports API-based evaluations across HuggingFace, local, and OpenAI-compatible endpoints, enabling apples-to-apples comparisons using config-driven workflows.

The best way to ensure reproducible LLM evaluation results is using a config-driven harness that standardizes prompts, few-shot setups, and metrics definitions. This unified approach guarantees consistent, comparable benchmarking across diverse models and standard tasks.

Yes, the LLM evaluation harness supports coding benchmarks including HumanEval and MBPP. It also covers diverse domains like TruthfulQA and ARC, allowing you to evaluate code generation and reasoning capabilities within the same reproducible workflow.

You configure few-shot settings for LLM benchmarking tasks through the harness's config-driven definitions. By specifying prompts and few-shot setups in the configuration, you ensure consistent task loading and reproducible metrics across all evaluated language models.

Benchmarking language models with a unified harness generates structured JSON and CSV results. These output files contain comparable metrics from standard tasks, enabling seamless downstream analytics, model comparison, and reporting.