evaluating-llms-harness

You can benchmark LLMs by running the lm-evaluation-harness with selected tasks like MMLU and GSM8K. Configure a backend such as HuggingFace or vLLM, then execute lm_eval with reproducible settings like few-shot count and batch size to quantify model quality.

To compare model quality across checkpoints, run the same standardized benchmark suites using lm-evaluation-harness. This tracks training progress and cross-model comparisons consistently by evaluating releases on tasks like TruthfulQA, HumanEval, and HellaSwag.

Yes, vLLM is supported as a high-throughput backend for LLM evaluation. You can configure lm-evaluation-harness to use vLLM alongside HuggingFace models to run 60+ standardized benchmarks measuring knowledge, reasoning, truthfulness, and code generation.

Run an evaluation on a HuggingFace model by executing a single lm_eval command specifying the model, tasks like mmlu, gsm8k, and hellaswag, the device such as cuda:0, and the batch size to ensure reproducible evaluation settings.

Yes, lm-evaluation-harness supports code generation benchmarks. It includes HumanEval as a standardized task to measure code generation capabilities alongside other benchmarks for knowledge, reasoning, and truthfulness across 60+ available tasks.

Before running an lm_eval benchmark, you need to install lm-eval, select appropriate task names, and configure the model backend such as HuggingFace or vLLM. Set reproducible parameters like few-shot count, device, and batch size to ensure consistent results.