{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Unified framework for evaluating generative language models against over 60 standard academic benchmarks with hundreds of task variants, supporting multiple model backends and inference engines.","skillfed_tags":["llm-evaluation","benchmarking","model-testing"],"use_cases":["Benchmark a new model against standard tasks to compare performance with published results.","Evaluate fine-tuned or quantized variants of a base model to measure the impact of training changes.","Run custom evaluation tasks defined in YAML with domain-specific prompts and metrics.","Batch-evaluate multiple models across different inference backends to find speed/accuracy tradeoffs.","Reproduce leaderboard-style evaluations locally for internal model development.","Integrate evaluation into CI/CD pipelines to track model quality metrics across checkpoints."],"what_it_does":"lm-eval is a standardized evaluation framework that runs language models against a collection of academic benchmarks and custom tasks. It abstracts away the boilerplate of loading models, preparing datasets, running inference, and computing metrics, letting researchers and practitioners focus on comparing model performance across consistent evaluation protocols.\n\nThe package supports multiple inference backends, each installed as an optional extra to keep the base installation lean. Tasks are defined via YAML configuration files with Jinja2 templating for prompt design, and the CLI provides subcommands to list available tasks, validate configurations, and run evaluations. It powers the Open LLM Leaderboard and has been cited in hundreds of research papers.","worth_installing":"Yes. lm-eval is the de facto standard for language model evaluation, actively maintained, permissively licensed, and designed with low friction\u2014base install is lightweight and model backends are optional. If you need to benchmark models against academic tasks or reproduce published results, this is the right tool. Install only if you have a model backend in mind and Python >=3.10."},"id":"lm-eval","links":{"html":"https://skillfed.io/packages/lm-eval","md":"https://skillfed.io/packages/lm-eval.md","pypi":"https://pypi.org/project/lm-eval/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-11","license_spdx":null,"license_treatment":"permissive","name":"lm-eval","python_support":"supports_current","summary":"A framework for evaluating language models"},"popularity":{"monthly_downloads":1572996,"position":3748,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.4.12"}