Hugging Face Evaluation

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)

When to Use

• You need to add structured evaluation results to a Hugging Face model card.
• You want to import benchmark data or run custom evaluations with vLLM, lighteval, or inspect-ai.
• You are preparing leaderboard-compatible model-index metadata for a model release.

Core Workflow

1. Confirm the request matches this skill's trigger, scope, and risk profile.
2. Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.
3. Load references/full-guidance.md when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.
4. Apply only the relevant guidance instead of loading or repeating the entire reference by default.
5. Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.

Topic Map

• Integration with HF Ecosystem
• Core Dependencies
• Inference Provider Evaluation
• vLLM Custom Model Evaluation (GPU required)
• ⚠️ CRITICAL: Check for Existing PRs Before Creating New Ones
• Inspect and Extract Evaluation Tables from README
• Import from Artificial Analysis
• Model-Index Management
• Run Evaluations on HF Jobs (Inference Providers)
• Run Custom Model Evaluations with vLLM (NEW)
• Before running the script
• Running the script
• Features
• Prerequisites
• Method 1: Extract from README (CLI workflow)
• Method 2: Import from Artificial Analysis
• Method 3: Run Evaluation Job
• Method 4: Run Custom Model Evaluation with vLLM

Reference Map

• references/full-guidance.md preserves the complete original guidance, including examples and detailed edge cases.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Progressive Loading

Keep this SKILL.md as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.