evaluating-llms-harness

To benchmark LLMs, you can use a unified evaluation harness to evaluate models across 60+ tasks like GSM8K and HumanEval with standardized prompts and metrics. Configure models and data flows via YAML and command-line options to achieve reproducible results.

Yes, you can use vLLM to run LLM benchmarks. The evaluation harness supports multiple backends, including HuggingFace, vLLM, and API-based interfaces, enabling flexible deployment and scalable benchmarking for your models.

The best way to compare model quality and track training progress is using a unified framework that benchmarks LLMs with standardized metrics. This approach enables reproducible comparisons and progress tracking across experiments using a common prompt format.

You configure tasks and models for LLM evaluation via YAML files and command-line options. This configuration method defines data flows and model parameters to achieve reproducible, shareable benchmarking results across different experiments.

Yes, the lm-evaluation-harness framework supports API-based models. It evaluates LLMs across 60+ benchmarks using multiple backends, including API-based interfaces, allowing you to benchmark remote models alongside local ones.