hugging-face-evaluation

name	hugging-face-evaluation
description	Add evaluation results to Hugging Face model repositories using the .eval_results/ format. Uses HF CLI for PR management and manual YAML creation.

Overview

This skill adds structured evaluation results to HuggingFace model repositories using the .eval_results/ format.

What This Enables:

• Results appear on model pages with benchmark links
• Scores are aggregated into benchmark dataset leaderboards
• Community contributions via Pull Requests

Important

Evaluation PRs can only be opened on the Hugging Face Hub. They cannot be opened on the GitHub repository.

Version

3.0.0

Workflow Overview

The actual workflow uses:

1. HF CLI (hf upload, hf download) for PR operations
2. Manual YAML creation in /tmp/pr-reviews/
3. check_prs.py script to check for existing PRs
4. curl to fetch model cards and leaderboard data

See references/hf_cli_for_prs.md for detailed CLI instructions.

---

CRITICAL: Multiple Scores for One Benchmark

Models can have multiple scores for the same benchmark (with/without tools). Each variant MUST be in a separate file.

File Naming Convention

Condition	File Name	Notes Field
Default (no tools)	hle.yaml	None (omit notes)
With tools	hle_with_tools.yaml	notes: "With tools"

Notes Field Rules

1. No tools = No notes field - Default assumption is "without tools"
2. With tools = Add notes - Only add when tools ARE used
3. Standardized format - Always use notes: "With tools" (capital W)

CORRECT:

# hle.yaml (no tools - DEFAULT)
- dataset:
    id: cais/hle
    task_id: hle
  value: 22.1
  source:
    url: https://huggingface.co/org/model
    name: Model Card
    user: username

# hle_with_tools.yaml (with tools)
- dataset:
    id: cais/hle
    task_id: hle
  value: 44.9
  source:
    url: https://huggingface.co/org/model
    name: Model Card
    user: username
  notes: "With tools"

INCORRECT:

notes: "Without tools"  # Don't add notes for default
notes: "w/ tools"       # Use standardized format
notes: "with tools"     # Capital W required

---

Core Workflow

Step 1: Check for Existing PRs

ALWAYS check before creating new PRs:

uv run scripts/check_prs.py --repo-id "org/model-name"

If PRs exist, update them instead of creating new ones.

Step 2: Fetch Model Card and Extract Scores

# Get model README
curl -s "https://huggingface.co/org/model-name/raw/main/README.md" | grep -i -A10 "HLE\|GPQA\|MMLU"

Or use MCP tools:

mcp__hf-mcp-server__hub_repo_details
  repo_ids: ["org/model-name"]
  include_readme: true

Step 3: Create YAML File

mkdir -p /tmp/pr-reviews/new-prs
cd /tmp/pr-reviews/new-prs

cat > hle.yaml << 'EOF'
- dataset:
    id: cais/hle
    task_id: hle
  value: 22.1
  date: '2026-02-03'
  source:
    url: https://huggingface.co/org/model-name
    name: Model Card
    user: burtenshaw
EOF

Step 4: Create PR

hf upload org/model-name hle.yaml .eval_results/hle.yaml \
  --repo-type model --create-pr \
  --commit-message "Add HLE evaluation result"

Step 5: Get PR Number

uv run scripts/check_prs.py --repo-id "org/model-name"

---

Updating Existing PRs

# Download PR contents
hf download org/model-name --repo-type model \
  --revision refs/pr/<PR_NUMBER> \
  --include ".eval_results/*" \
  --local-dir /tmp/pr-reviews/model-pr<PR_NUMBER>

# Edit the YAML file, then upload
hf upload org/model-name /tmp/pr-reviews/updated.yaml .eval_results/hle.yaml \
  --repo-type model \
  --revision refs/pr/<PR_NUMBER> \
  --commit-message "Update evaluation result"

---

Deleting Files from PRs

Use Python API:

uv run --with huggingface_hub python3 << 'EOF'
from huggingface_hub import HfApi
api = HfApi()
api.delete_file(
    path_in_repo=".eval_results/old_file.yaml",
    repo_id="org/model-name",
    repo_type="model",
    revision="refs/pr/<PR_NUMBER>",
    commit_message="Remove file"
)
EOF

---

Fetching Leaderboard Data

# HLE leaderboard (requires auth for private datasets)
curl -s "https://huggingface.co/api/datasets/cais/hle/leaderboard" \
  -H "Authorization: Bearer $HF_TOKEN"

# MMLU-Pro leaderboard (public)
curl -s "https://huggingface.co/api/datasets/TIGER-Lab/MMLU-Pro/leaderboard"

# Model eval results
curl -s "https://huggingface.co/api/models/org/model?expand[]=evalResults"

---

.eval_results/ Format

# .eval_results/hle.yaml
- dataset:
    id: cais/hle              # Required: Hub Benchmark dataset ID
    task_id: hle              # Required: task id from dataset's eval.yaml
  value: 22.2                 # Required: metric value
  date: "2026-01-14"          # Optional: ISO-8601 date
  source:                     # Optional: attribution
    url: https://huggingface.co/org/model
    name: Model Card
    user: username

---

Supported Benchmarks

Benchmark	Hub Dataset ID	Task ID
HLE	cais/hle	hle
GPQA	Idavidrein/gpqa	diamond
MMLU-Pro	TIGER-Lab/MMLU-Pro	mmlu_pro

---

Tool-Using Agent Models

Models like MiroThinker, Nemotron-Orchestrator are inherently tool-using agents. For these:

1. Use hle_with_tools.yaml as filename
2. Add notes: "With tools"
3. Look for terms: "search agent", "agentic", "orchestrator", "code-interpreter"

---

Environment Setup

export HF_TOKEN="your-huggingface-token"

---

Scripts Reference

# Check for existing PRs (ALWAYS do this first)
uv run scripts/check_prs.py --repo-id "org/model-name"

See references/hf_cli_for_prs.md for complete HF CLI workflow documentation.

---

Best Practices

1. Always check for existing PRs before creating new ones
2. Separate files for variants - hle.yaml for default, hle_with_tools.yaml for tools
3. Notes only for non-default - Omit notes for standard evaluations
4. Standardized format - Use "With tools" exactly (capital W)
5. Verify scores - Compare YAML against model card before submitting