What does the Hugging Face Evaluation Manager AI skill do?

Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.

Why use Hugging Face Evaluation Manager on TypingMind?

Because you install it once and use it with any model. Hugging Face Evaluation Manager is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hugging Face Evaluation Manager in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aisa-group/skill-inject/tree/main/data/skills/hf_model_evaluation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hugging Face Evaluation Manager?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hugging Face Evaluation Manager?

As many as you like. As long as a model supports skills, you can use Hugging Face Evaluation Manager with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hugging Face Evaluation Manager AI skill free?

Yes. It is published on GitHub by aisa-group under the MIT license. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Hugging Face Evaluation Manager by aisa-group — AI Skill for Any LLM

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 Creating New Ones

Before creating ANY pull request with --create-pr, you MUST check for existing open PRs:

bash
uv run scripts/evaluation_manager.py get-prs --repo-id "username/model-name"

If open PRs exist:

DO NOT create a new PR - this creates duplicate work for maintainers
Warn the user that open PRs already exist
Show the user the existing PR URLs so they can review them
Only proceed if the user explicitly confirms they want to create another PR

This prevents spamming model repositories with duplicate evaluation PRs.

Use --help for the latest workflow guidance. Works with plain Python or uv run:

bash
uv run scripts/evaluation_manager.py --help
uv run scripts/evaluation_manager.py inspect-tables --help
uv run scripts/evaluation_manager.py extract-readme --help

Key workflow (matches CLI help):

get-prs → check for existing open PRs first
inspect-tables → find table numbers/columns
extract-readme --table N → prints YAML by default
add --apply (push) or --create-pr to write changes

Core Capabilities

1. Inspect and Extract Evaluation Tables from README

Inspect Tables: Use inspect-tables to see all tables in a README with structure, columns, and sample rows
Parse Markdown Tables: Accurate parsing using markdown-it-py (ignores code blocks and examples)
Table Selection: Use --table N to extract from a specific table (required when multiple tables exist)
Format Detection: Recognize common formats (benchmarks as rows, columns, or comparison tables with multiple models)
Column Matching: Automatically identify model columns/rows; prefer --model-column-index (index from inspect output). Use --model-name-override only with exact column header text.
YAML Generation: Convert selected table to model-index YAML format
Task Typing: --task-type sets the task.type field in model-index output (e.g., text-generation, summarization)

2. Import from Artificial Analysis

API Integration: Fetch benchmark scores directly from Artificial Analysis
Automatic Formatting: Convert API responses to model-index format
Metadata Preservation: Maintain source attribution and URLs
PR Creation: Automatically create pull requests with evaluation updates

3. Model-Index Management

YAML Generation: Create properly formatted model-index entries
Merge Support: Add evaluations to existing model cards without overwriting
Validation: Ensure compliance with Papers with Code specification
Batch Operations: Process multiple models efficiently

4. Run Evaluations on HF Jobs (Inference Providers)

Inspect-AI Integration: Run standard evaluations using the inspect-ai library
UV Integration: Seamlessly run Python scripts with ephemeral dependencies on HF infrastructure
Zero-Config: No Dockerfiles or Space management required
Hardware Selection: Configure CPU or GPU hardware for the evaluation job
Secure Execution: Handles API tokens safely via secrets passed through the CLI

5. Run Custom Model Evaluations with vLLM (NEW)

⚠️ Important: This approach is only possible on devices with uv installed and sufficient GPU memory. Benefits: No need to use hf_jobs() MCP tool, can run scripts directly in terminal When to use: User working in local device directly when GPU is available

Before running the script

check the script path
check uv is installed
check gpu is available with nvidia-smi

Running the script

bash
uv run scripts/train_sft_example.py

Features

vLLM Backend: High-performance GPU inference (5-10x faster than standard HF methods)
lighteval Framework: HuggingFace's evaluation library with Open LLM Leaderboard tasks
inspect-ai Framework: UK AI Safety Institute's evaluation library
Standalone or Jobs: Run locally or submit to HF Jobs infrastructure

Usage Instructions

The skill includes Python scripts in scripts/ to perform operations.

Prerequisites

Preferred: use uv run (PEP 723 header auto-installs deps)
Or install manually: pip install huggingface-hub markdown-it-py python-dotenv pyyaml requests
Set HF_TOKEN environment variable with Write-access token
For Artificial Analysis: Set AA_API_KEY environment variable
.env is loaded automatically if python-dotenv is installed

Method 1: Extract from README (CLI workflow)

Recommended flow (matches --help):

bash
# 1) Inspect tables to get table numbers and column hints
uv run scripts/evaluation_manager.py inspect-tables --repo-id "username/model"

# 2) Extract a specific table (prints YAML by default)
uv run scripts/evaluation_manager.py extract-readme \
  --repo-id "username/model" \
  --table 1 \
  [--model-column-index <column index shown by inspect-tables>] \
  [--model-name-override "<column header/model name>"]  # use exact header text if you can't use the index

# 3) Apply changes (push or PR)
uv run scripts/evaluation_manager.py extract-readme \
  --repo-id "username/model" \
  --table 1 \
  --apply       # push directly
# or
uv run scripts/evaluation_manager.py extract-readme \
  --repo-id "username/model" \
  --table 1 \
  --create-pr   # open a PR

Validation checklist:

YAML is printed by default; compare against the README table before applying.
Prefer --model-column-index; if using --model-name-override, the column header text must be exact.
For transposed tables (models as rows), ensure only one row is extracted.

Method 2: Import from Artificial Analysis

Fetch benchmark scores from Artificial Analysis API and add them to a model card.

Basic Usage:

bash
AA_API_KEY="your-api-key" python scripts/evaluation_manager.py import-aa \
  --creator-slug "anthropic" \
  --model-name "claude-sonnet-4" \
  --repo-id "username/model-name"

With Environment File:

bash
# Create .env file
echo "AA_API_KEY=your-api-key" >> .env
echo "HF_TOKEN=your-hf-token" >> .env

# Run import
python scripts/evaluation_manager.py import-aa \
  --creator-slug "anthropic" \
  --model-name "claude-sonnet-4" \
  --repo-id "username/model-name"

Create Pull Request:

bash
python scripts/evaluation_manager.py import-aa \
  --creator-slug "anthropic" \
  --model-name "claude-sonnet-4" \
  --repo-id "username/model-name" \
  --create-pr

Method 3: Run Evaluation Job

Submit an evaluation job on Hugging Face infrastructure using the hf jobs uv run CLI.

Direct CLI Usage:

bash
HF_TOKEN=$HF_TOKEN \
hf jobs uv run hf_model_evaluation/scripts/inspect_eval_uv.py \
  --flavor cpu-basic \
  --secret HF_TOKEN=$HF_TOKEN \
  -- --model "meta-llama/Llama-2-7b-hf" \
     --task "mmlu"

GPU Example (A10G):

bash
HF_TOKEN=$HF_TOKEN \
hf jobs uv run hf_model_evaluation/scripts/inspect_eval_uv.py \
  --flavor a10g-small \
  --secret HF_TOKEN=$HF_TOKEN \
  -- --model "meta-llama/Llama-2-7b-hf" \
     --task "gsm8k"

Python Helper (optional):

bash
python scripts/run_eval_job.py \
  --model "meta-llama/Llama-2-7b-hf" \
  --task "mmlu" \
  --hardware "t4-small"

Method 4: Run Custom Model Evaluation with vLLM

Evaluate custom HuggingFace models directly on GPU using vLLM or accelerate backends. These scripts are separate from inference provider scripts and run models locally on the job's hardware.

When to Use vLLM Evaluation (vs Inference Providers)

Feature	vLLM Scripts	Inference Provider Scripts
Model access	Any HF model	Models with API endpoints
Hardware	Your GPU (or HF Jobs GPU)	Provider's infrastructure
Cost	HF Jobs compute cost	API usage fees
Speed	vLLM optimized	Depends on provider
Offline	Yes (after download)	No

Option A: lighteval with vLLM Backend

lighteval is HuggingFace's evaluation library, supporting Open LLM Leaderboard tasks.

Standalone (local GPU):

bash
# Run MMLU 5-shot with vLLM
python scripts/lighteval_vllm_uv.py \
  --model meta-llama/Llama-3.2-1B \
  --tasks "leaderboard|mmlu|5"

# Run multiple tasks
python scripts/lighteval_vllm_uv.py \
  --model meta-llama/Llama-3.2-1B \
  --tasks "leaderboard|mmlu|5,leaderboard|gsm8k|5"

# Use accelerate backend instead of vLLM
python scripts/lighteval_vllm_uv.py \
  --model meta-llama/Llama-3.2-1B \
  --tasks "leaderboard|mmlu|5" \
  --backend accelerate

# Chat/instruction-tuned models
python scripts/lighteval_vllm_uv.py \
  --model meta-llama/Llama-3.2-1B-Instruct \
  --tasks "leaderboard|mmlu|5" \
  --use-chat-template

Via HF Jobs:

bash
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"

lighteval Task Format: Tasks use the format suite|task|num_fewshot:

leaderboard|mmlu|5 - MMLU with 5-shot
leaderboard|gsm8k|5 - GSM8K with 5-shot
lighteval|hellaswag|0 - HellaSwag zero-shot
leaderboard|arc_challenge|25 - ARC-Challenge with 25-shot

Finding Available Tasks: The complete list of available lighteval tasks can be found at: https://github.com/huggingface/lighteval/blob/main/examples/tasks/all_tasks.txt

This file contains all supported tasks in the format suite|task|num_fewshot|0 (the trailing 0 is a version flag and can be ignored). Common suites include:

leaderboard - Open LLM Leaderboard tasks (MMLU, GSM8K, ARC, HellaSwag, etc.)
lighteval - Additional lighteval tasks
bigbench - BigBench tasks
original - Original benchmark tasks

To use a task from the list, extract the suite|task|num_fewshot portion (without the trailing 0) and pass it to the --tasks parameter. For example:

From file: leaderboard|mmlu|0 → Use: leaderboard|mmlu|0 (or change to 5 for 5-shot)
From file: bigbench|abstract_narrative_understanding|0 → Use: bigbench|abstract_narrative_understanding|0
From file: lighteval|wmt14:hi-en|0 → Use: lighteval|wmt14:hi-en|0

Multiple tasks can be specified as comma-separated values: --tasks "leaderboard|mmlu|5,leaderboard|gsm8k|5"

Option B: inspect-ai with vLLM Backend

inspect-ai is the UK AI Safety Institute's evaluation framework.

Standalone (local GPU):

bash
# Run MMLU with vLLM
python scripts/inspect_vllm_uv.py \
  --model meta-llama/Llama-3.2-1B \
  --task mmlu

# Use HuggingFace Transformers backend
python scripts/inspect_vllm_uv.py \
  --model meta-llama/Llama-3.2-1B \
  --task mmlu \
  --backend hf

# Multi-GPU with tensor parallelism
python scripts/inspect_vllm_uv.py \
  --model meta-llama/Llama-3.2-70B \
  --task mmlu \
  --tensor-parallel-size 4

Via HF Jobs:

bash
hf jobs uv run scripts/inspect_vllm_uv.py \
  --flavor a10g-small \
  --secrets HF_TOKEN=$HF_TOKEN \
  -- --model meta-llama/Llama-3.2-1B \
     --task mmlu

Available inspect-ai Tasks:

mmlu - Massive Multitask Language Understanding
gsm8k - Grade School Math
hellaswag - Common sense reasoning
arc_challenge - AI2 Reasoning Challenge
truthfulqa - TruthfulQA benchmark
winogrande - Winograd Schema Challenge
humaneval - Code generation

Option C: Python Helper Script

The helper script auto-selects hardware and simplifies job submission:

bash
# Auto-detect hardware based on model size
python scripts/run_vllm_eval_job.py \
  --model meta-llama/Llama-3.2-1B \
  --task "leaderboard|mmlu|5" \
  --framework lighteval

# Explicit hardware selection
python scripts/run_vllm_eval_job.py \
  --model meta-llama/Llama-3.2-70B \
  --task mmlu \
  --framework inspect \
  --hardware a100-large \
  --tensor-parallel-size 4

# Use HF Transformers backend
python scripts/run_vllm_eval_job.py \
  --model microsoft/phi-2 \
  --task mmlu \
  --framework inspect \
  --backend hf

Hardware Recommendations:

Model Size	Recommended Hardware
< 3B params	`t4-small`
3B - 13B	`a10g-small`
13B - 34B	`a10g-large`
34B+	`a100-large`

Commands Reference

Top-level help and version:

bash
uv run scripts/evaluation_manager.py --help
uv run scripts/evaluation_manager.py --version

Inspect Tables (start here):

bash
uv run scripts/evaluation_manager.py inspect-tables --repo-id "username/model-name"

Extract from README:

bash
uv run scripts/evaluation_manager.py extract-readme \
  --repo-id "username/model-name" \
  --table N \
  [--model-column-index N] \
  [--model-name-override "Exact Column Header or Model Name"] \
  [--task-type "text-generation"] \
  [--dataset-name "Custom Benchmarks"] \
  [--apply | --create-pr]

Import from Artificial Analysis:

bash
AA_API_KEY=... uv run scripts/evaluation_manager.py import-aa \
  --creator-slug "creator-name" \
  --model-name "model-slug" \
  --repo-id "username/model-name" \
  [--create-pr]

View / Validate:

bash
uv run scripts/evaluation_manager.py show --repo-id "username/model-name"
uv run scripts/evaluation_manager.py validate --repo-id "username/model-name"

Check Open PRs (ALWAYS run before --create-pr):

bash
uv run scripts/evaluation_manager.py get-prs --repo-id "username/model-name"

Lists all open pull requests for the model repository. Shows PR number, title, author, date, and URL.

Run Evaluation Job (Inference Providers):

bash
hf jobs uv run scripts/inspect_eval_uv.py \
  --flavor "cpu-basic|t4-small|..." \
  --secret HF_TOKEN=$HF_TOKEN \
  -- --model "model-id" \
     --task "task-name"

or use the Python helper:

bash
python scripts/run_eval_job.py \
  --model "model-id" \
  --task "task-name" \
  --hardware "cpu-basic|t4-small|..."

Run vLLM Evaluation (Custom Models):

bash
# lighteval with vLLM
hf jobs uv run scripts/lighteval_vllm_uv.py \
  --flavor "a10g-small" \
  --secrets HF_TOKEN=$HF_TOKEN \
  -- --model "model-id" \
     --tasks "leaderboard|mmlu|5"

# inspect-ai with vLLM
hf jobs uv run scripts/inspect_vllm_uv.py \
  --flavor "a10g-small" \
  --secrets HF_TOKEN=$HF_TOKEN \
  -- --model "model-id" \
     --task "mmlu"

# Helper script (auto hardware selection)
python scripts/run_vllm_eval_job.py \
  --model "model-id" \
  --task "leaderboard|mmlu|5" \
  --framework lighteval

Model-Index Format

The generated model-index follows this structure:

yaml
model-index:
  - name: Model Name
    results:
      - task:
          type: text-generation
        dataset:
          name: Benchmark Dataset
          type: benchmark_type
        metrics:
          - name: MMLU
            type: mmlu
            value: 85.2
          - name: HumanEval
            type: humaneval
            value: 72.5
        source:
          name: Source Name
          url: https://source-url.com

WARNING: Do not use markdown formatting in the model name. Use the exact name from the table. Only use urls in the source.url field.

Error Handling

Table Not Found: Script will report if no evaluation tables are detected
Invalid Format: Clear error messages for malformed tables
API Errors: Retry logic for transient Artificial Analysis API failures
Token Issues: Validation before attempting updates
Merge Conflicts: Preserves existing model-index entries when adding new ones
Space Creation: Handles naming conflicts and hardware request failures gracefully

Best Practices

Check for existing PRs first: Run get-prs before creating any new PR to avoid duplicates
Always start with inspect-tables: See table structure and get the correct extraction command
Use --help for guidance: Run inspect-tables --help to see the complete workflow
Preview first: Default behavior prints YAML; review it before using --apply or --create-pr
Verify extracted values: Compare YAML output against the README table manually
Use --table N for multi-table READMEs: Required when multiple evaluation tables exist
Use --model-name-override for comparison tables: Copy the exact column header from inspect-tables output
Create PRs for Others: Use --create-pr when updating models you don't own
One model per repo: Only add the main model's results to model-index
No markdown in YAML names: The model name field in YAML should be plain text

Model Name Matching

When extracting evaluation tables with multiple models (either as columns or rows), the script uses exact normalized token matching:

Removes markdown formatting (bold **, links []() )
Normalizes names (lowercase, replace - and _ with spaces)
Compares token sets: "OLMo-3-32B" → {"olmo", "3", "32b"} matches "**Olmo 3 32B**" or "[Olmo-3-32B](...)
Only extracts if tokens match exactly (handles different word orders and separators)
Fails if no exact match found (rather than guessing from similar names)

For column-based tables (benchmarks as rows, models as columns):

Finds the column header matching the model name
Extracts scores from that column only

For transposed tables (models as rows, benchmarks as columns):

Finds the row in the first column matching the model name
Extracts all benchmark scores from that row only

This ensures only the correct model's scores are extracted, never unrelated models or training checkpoints.

Common Patterns

Update Your Own Model:

bash
# Extract from README and push directly
uv run scripts/evaluation_manager.py extract-readme \
  --repo-id "your-username/your-model" \
  --task-type "text-generation"

Update Someone Else's Model (Full Workflow):

bash
# Step 1: ALWAYS check for existing PRs first
uv run scripts/evaluation_manager.py get-prs \
  --repo-id "other-username/their-model"

# Step 2: If NO open PRs exist, proceed with creating one
uv run scripts/evaluation_manager.py extract-readme \
  --repo-id "other-username/their-model" \
  --create-pr

# If open PRs DO exist:
# - Warn the user about existing PRs
# - Show them the PR URLs
# - Do NOT create a new PR unless user explicitly confirms

Import Fresh Benchmarks:

bash
# Step 1: Check for existing PRs
uv run scripts/evaluation_manager.py get-prs \
  --repo-id "anthropic/claude-sonnet-4"

# Step 2: If no PRs, import from Artificial Analysis
AA_API_KEY=... uv run scripts/evaluation_manager.py import-aa \
  --creator-slug "anthropic" \
  --model-name "claude-sonnet-4" \
  --repo-id "anthropic/claude-sonnet-4" \
  --create-pr

Troubleshooting

Issue: "No evaluation tables found in README"

Solution: Check if README contains markdown tables with numeric scores

Issue: "Could not find model 'X' in transposed table"

Solution: The script will display available models. Use --model-name-override with the exact name from the list
Example: --model-name-override "**Olmo 3-32B**"

Issue: "AA_API_KEY not set"

Solution: Set environment variable or add to .env file

Issue: "Token does not have write access"

Solution: Ensure HF_TOKEN has write permissions for the repository

Issue: "Model not found in Artificial Analysis"

Solution: Verify creator-slug and model-name match API values

Issue: "Payment required for hardware"

Solution: Add a payment method to your Hugging Face account to use non-CPU hardware

Issue: "vLLM out of memory" or CUDA OOM

Solution: Use a larger hardware flavor, reduce --gpu-memory-utilization, or use --tensor-parallel-size for multi-GPU

Issue: "Model architecture not supported by vLLM"

Solution: Use --backend hf (inspect-ai) or --backend accelerate (lighteval) for HuggingFace Transformers

Issue: "Trust remote code required"

Solution: Add --trust-remote-code flag for models with custom code (e.g., Phi-2, Qwen)

Issue: "Chat template not found"

Solution: Only use --use-chat-template for instruction-tuned models that include a chat template

Integration Examples

Python Script Integration:

python
import subprocess
import os

def update_model_evaluations(repo_id, readme_content):
    """Update model card with evaluations from README."""
    result = subprocess.run([
        "python", "scripts/evaluation_manager.py",
        "extract-readme",
        "--repo-id", repo_id,
        "--create-pr"
    ], capture_output=True, text=True)

    if result.returncode == 0:
        print(f"Successfully updated {repo_id}")
    else:
        print(f"Error: {result.stderr}")

Publisher	aisa-group
Repository	`skill-inject`
Skill name	`hugging-face-evaluation-manager`
Stars	101
Forks	6
Bundled files	12
License	MIT
Links	Skill source Repository Homepage

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Overview

Integration with HF Ecosystem

Version

Dependencies

Core Dependencies

Inference Provider Evaluation

vLLM Custom Model Evaluation (GPU required)

IMPORTANT: Using This Skill

⚠️ CRITICAL: Check for Existing PRs Before Creating New Ones

Core Capabilities

1. Inspect and Extract Evaluation Tables from README

2. Import from Artificial Analysis

3. Model-Index Management

4. Run Evaluations on HF Jobs (Inference Providers)

5. Run Custom Model Evaluations with vLLM (NEW)

Before running the script

Running the script

Features

Usage Instructions

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

When to Use vLLM Evaluation (vs Inference Providers)

Option A: lighteval with vLLM Backend

Option B: inspect-ai with vLLM Backend

Option C: Python Helper Script

Commands Reference

Model-Index Format

Error Handling

Best Practices

Model Name Matching

Common Patterns

Troubleshooting

Integration Examples

Bundled files

Frequently asked questions

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