Framework for Few-Shot Evaluation of Language Models

lm-evaluation-harness is a framework designed for the few-shot evaluation of language models. It provides a unified interface to test generative models across a variety of evaluation tasks, ensuring reproducibility and comparability in research.

Key features:

• Over 60 standard benchmarks - Includes hundreds of subtasks and variants for comprehensive evaluation.
• Support for multiple model backends - Compatible with models from Hugging Face, GPT-NeoX, and Megatron-DeepSpeed.
• Flexible tokenization - Offers a tokenization-agnostic interface for ease of use.
• Fast inference - Optimized for memory efficiency with support for vLLM.
• Custom prompts and metrics - Allows users to define their own evaluation criteria.

The framework is widely used in academic research and by organizations such as NVIDIA and Cohere, making it a vital tool for evaluating the performance of language models.