hugging-face-evaluationlisted

# Overview This skill provides tools to add structured evaluation results to Hugging Face model cards. It supports multiple methods for adding evaluation data: - Extracting existing evaluation tables from README content - Importing benchmark scores from Artificial Analysis - Running custom model evaluations with vLLM or accelerate backends (lighteval/inspect-ai) ## Integration with HF Ecosystem - **Model Cards**: Updates model-index metadata for leaderboard integration - **Artificial Analysis**: Direct API integration for benchmark imports - **Papers with Code**: Compatible with their model-index specification - **Jobs**: Run evaluations directly on Hugging Face Jobs with `uv` integration - **vLLM**: Efficient GPU inference for custom model evaluation - **lighteval**: HuggingFace's evaluation library with vLLM/accelerate backends - **inspect-ai**: UK AI Safety Institute's evaluation framework # Version 1.3.0 # Dependencies ## Core Dependencies - huggingface_hub>=0.26.0 - markdown-it-py>=3.0.0 - python-dotenv>=1.2.1 - pyyaml>=6.0.3 - requests>=2.32.5 - re (built-in) ## Inference Provider Evaluation - inspect-ai>=0.3.0 - inspect-evals - openai ## vLLM Custom Model Evaluation (GPU required) - lighteval[accelerate,vllm]>=0.6.0 - vllm>=0.4.0 - torch>=2.0.0 - transformers>=4.40.0 - accelerate>=0.30.0 Note: vLLM dependencies are installed automatically via PEP 723 script headers when using `uv run`. # IMPORTANT: Using This Skill ## ⚠️ CRITICAL: Check for Existing PRs Before