evaluating-llms-harness

You can evaluate large language models on academic benchmarks like MMLU using this Skill by specifying the HuggingFace model and target tasks in the lm-evaluation-harness command. It supports evaluating models across 60+ benchmarks to generate performance metrics.

Yes, you can evaluate API-based models from OpenAI and Anthropic. The Skill supports API evaluation alongside local HuggingFace and vLLM models, allowing you to compare different models on multiple tasks to identify their strengths and weaknesses.

You need lm-eval, transformers, and vllm dependencies installed to run large language model benchmarking. These libraries provide the framework for loading models, executing inference, and evaluating performance across 60+ academic benchmarks.

Model comparison works by evaluating different models on multiple tasks to identify their strengths and weaknesses. You can run benchmarks across 60+ academic tasks, track training progress by evaluating checkpoints, and report comprehensive academic results.

Yes, you can create custom evaluation tasks tailored to specific domains. This Skill supports custom tasks alongside the 60+ built-in academic benchmarks, enabling comprehensive evaluation of large language models for specialized use cases.