evaluating-llms-harness

You can benchmark large language models on MMLU and GSM8K using the lm-evaluation-harness suite, which runs 60+ academic tasks with a single command to generate detailed JSON reports for model comparison.

Yes, you can use vLLM to accelerate inference during LLM evaluation by installing it as an optional dependency in your Python environment alongside lm-eval and transformers for flexible backend support.

Yes, lm-evaluation-harness supports API-based services like OpenAI and Anthropic, allowing you to evaluate large language models remotely without requiring local HuggingFace model weights.

The best way to compare multiple LLMs across academic benchmarks is running a unified evaluation suite that generates detailed JSON reports and visualizes training curves for reproducible model comparison.

Yes, you need a Python environment with lm-eval and transformers installed to run lm-evaluation-harness benchmarks, plus optional vllm for accelerated inference when evaluating large language models.