instruct-eval

Overview

Community framework for quantitative evaluation of instruction-tuned models (e.g., Alpaca, Flan-T5) on held-out tasks. It provides a standardized benchmarking setup to measure model performance on unseen instructions.

Best for

Best for
Researchers and developers who need a simple, standardized way to evaluate instruction-tuned language models

Use cases

• Evaluate instruction-tuned models on a held-out task set
• Benchmark custom instruction-tuned models against baselines
• Compare output quality across different instruction-tuned architectures

Notes

Community framework for quantitative evaluation of instruction-tuned models (e.g., Alpaca, Flan-T5) on held-out tasks. It provides a standardized benchmarking setup to measure model performance on unseen instructions.

553 stars on GitHub. Last updated 2024-03-10. Licensed Apache-2.0.

Use cases

• Evaluate instruction-tuned models on a held-out task set
• Benchmark custom instruction-tuned models against baselines
• Compare output quality across different instruction-tuned architectures

Pros

• Lightweight and focused solely on evaluation
• Open source with community support
• Provides a consistent, reproducible evaluation pipeline

Cons

• Limited to instruction-tuned models only
• May not cover all evaluation metrics needed for production
• Requires manual integration with specific model formats

Indexed from awesome-llm and enriched against its public facts.