Project Title

lm-evaluation-harness — A Comprehensive Framework for Few-Shot Evaluation of Language Models

Overview

lm-evaluation-harness is a Python-based framework designed to evaluate the performance of generative language models across a multitude of tasks. It stands out for its extensive support for various model types, including commercial APIs and local models, and its ability to ensure reproducibility and comparability in language model evaluations.

Key Features

Over 60 standard academic benchmarks for LLMs, with hundreds of subtasks and variants.
Supports models loaded via transformers, GPT-NeoX, Megatron-DeepSpeed, and vLLM for fast and memory-efficient inference.
Includes commercial API support for OpenAI and TextSynth, along with evaluation on adapters like LoRA.
Enables evaluation with publicly available prompts for reproducibility and comparability.

Use Cases

Researchers and developers use lm-evaluation-harness to benchmark and compare the performance of different language models on a variety of tasks.
It serves as a tool for testing the capabilities of new language models before deployment in real-world applications.
Educational institutions can utilize it to teach and demonstrate the intricacies of language model evaluation and performance metrics.

Advantages

Broad compatibility with different model types and APIs, allowing for a wide range of evaluations.
The framework's design ensures that evaluations are reproducible and comparable, which is crucial for academic research and industry benchmarking.
Continuous updates and community involvement keep the project at the forefront of language model evaluation tools.

Limitations / Considerations

The project's complexity might require a steep learning curve for new users.
As with any evaluation tool, the accuracy of results depends on the quality and relevance of the benchmarks and tasks included.

BIG-bench: A benchmark for assessing large language models, but without the same level of framework support for model evaluation as lm-evaluation-harness.
Evaluating-Large-Language-Models: A collection of tasks for evaluating large language models, focusing more on specific tasks rather than providing a comprehensive evaluation framework.
lmms-eval: A project that extends lm-evaluation-harness with a broader range of multimodal tasks, models, and features.

Basic Information

GitHub: https://github.com/EleutherAI/lm-evaluation-harness
Stars: 10,169
License: Unknown
Last Commit: 2025-09-21

📊 Project Information

Project Name: lm-evaluation-harness
GitHub URL: https://github.com/EleutherAI/lm-evaluation-harness
Programming Language: Python
⭐ Stars: 10,169
🍴 Forks: 2,742
📅 Created: 2020-08-28
🔄 Last Updated: 2025-09-21

🏷️ Project Topics

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📚 Documentation

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lm-evaluation-harness

Project Description