mediar-ai/packages/skills/skills/huggingface-evaluation
packages/skills/skills/huggingface-evaluation/SKILL.md
# Hugging Face Evaluation Add structured evaluation results to model cards with support for README extraction, Artificial Analysis API, and custom evaluations using vLLM/lighteval. ## Prerequisites - uv (Python package manager) - HF_TOKEN environment variable - For Artificial Analysis: AA_API_KEY environment variable ## Instructions ### Workflow: Extract from README ```bash # 1. Check for existing PRs first uv run scripts/evaluation_manager.py get-prs --repo-id "username/model-name" # 2.
$ install --global
skillsauthnpx skillsauth add mediar-ai/skillhubz packages/skills/skills/huggingface-evaluationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
Hugging Face Evaluation
Add structured evaluation results to model cards with support for README extraction, Artificial Analysis API, and custom evaluations using vLLM/lighteval.
Prerequisites
- uv (Python package manager)
- HF_TOKEN environment variable
- For Artificial Analysis: AA_API_KEY environment variable
Instructions
Workflow: Extract from README
# 1. Check for existing PRs first
uv run scripts/evaluation_manager.py get-prs --repo-id "username/model-name"
# 2. Inspect tables to find evaluation data
uv run scripts/evaluation_manager.py inspect-tables --repo-id "username/model"
# 3. Extract specific table (prints YAML)
uv run scripts/evaluation_manager.py extract-readme \
--repo-id "username/model" \
--table 1
# 4. Apply changes
uv run scripts/evaluation_manager.py extract-readme \
--repo-id "username/model" \
--table 1 \
--create-pr # or --apply for direct push
Import from Artificial Analysis
AA_API_KEY="your-key" uv run scripts/evaluation_manager.py import-aa \
--creator-slug "anthropic" \
--model-name "claude-sonnet-4" \
--repo-id "username/model-name" \
--create-pr
Run Custom Evaluations
lighteval with vLLM:
uv run scripts/lighteval_vllm_uv.py \
--model meta-llama/Llama-3.2-1B \
--tasks "leaderboard|mmlu|5"
inspect-ai with vLLM:
uv run scripts/inspect_vllm_uv.py \
--model meta-llama/Llama-3.2-1B \
--task mmlu
Via HF Jobs:
hf jobs uv run scripts/lighteval_vllm_uv.py \
--flavor a10g-small \
--secrets HF_TOKEN=$HF_TOKEN \
-- --model meta-llama/Llama-3.2-1B \
--tasks "leaderboard|mmlu|5"
Hardware Recommendations
| Model Size | Hardware | |------------|----------| | < 3B params | t4-small | | 3B - 13B | a10g-small | | 13B - 34B | a10g-large | | 34B+ | a100-large |
Notes
- Always check for existing PRs before creating new ones
- Preview YAML output before applying changes
- vLLM provides 5-10x faster inference than standard methods
Source: huggingface/skills
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