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Huggingface Community Evals

Hugging FaceOfficialPublished by Hugging Face

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
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  • Has scripts

From the author

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
— the description by Hugging Face

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to running local evaluations for models hosted on the Hugging Face Hub. It leverages inspect-ai and lighteval frameworks to perform benchmarks, offering guidance on selecting appropriate inference backends like vLLM, Transformers, or accelerate. The skill is designed for local hardware execution, focusing on smoke testing and backend compatibility before scaling up. It explicitly excludes remote job orchestration, model card management, and automated result publishing, serving as a foundational tool for local model assessment.

What it does

  • Executes local evaluations using inspect-ai and lighteval.
  • Supports backend selection between vLLM, Transformers, and accelerate.
  • Provides scripts for smoke testing models with limited sample sizes.
  • Offers guidance on hardware requirements based on model size.
  • Facilitates troubleshooting for common CUDA, vLLM, and gated model access issues.
  • Defines clear boundaries for handing off tasks to remote job orchestration.

When to use it

  • When you need to perform quick local smoke tests on a Hugging Face model.
  • When evaluating model performance using specific benchmarks like MMLU or GSM8K.
  • When you need to determine the best inference backend for a specific model architecture.
  • When you are preparing to run larger evaluations and need to validate the setup locally first.
  • When you require explicit task control or leaderboard-style evaluation metrics.

Advantages

  • Enables rapid iteration through local smoke tests.
  • Provides flexible backend options to handle various model architectures.
  • Reduces overhead by utilizing local hardware for initial validation.
  • Clear separation between local testing and remote job execution workflows.
  • Standardized scripts ensure consistent evaluation patterns.

Usage examples

“Run a local smoke test on Llama-3.2-1B using inspect-ai for the MMLU task.”
The agent executes the inspect_eval_uv.py script with the specified model and task, limiting the run to 20 samples for a quick validation.
“Evaluate Phi-2 on GSM8K using vLLM locally.”
The agent runs the inspect_vllm_uv.py script, configuring the backend for vLLM to assess the model's performance on the GSM8K benchmark.
“Run a leaderboard-style evaluation for Llama-3.2-3B using lighteval.”
The agent utilizes lighteval_vllm_uv.py to run the specified leaderboard tasks, applying the chat template and limiting samples for a local test run.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/huggingface/huggingface-community-evals/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip huggingface-community-evals.zip -d .agents/skills

Documentation by Hugging Face

Overview

This skill is for running evaluations against models on the Hugging Face Hub on local hardware.

It covers:

  • inspect-ai with local inference

  • lighteval with local inference

  • choosing between vllm, Hugging Face Transformers, and accelerate

  • smoke tests, task selection, and backend fallback strategy

It does not cover:

  • Hugging Face Jobs orchestration

  • model-card or model-index edits

  • README table extraction

  • Artificial Analysis imports

  • .eval_results generation or publishing

  • PR creation or community-evals automation

If the user wants to run the same eval remotely on Hugging Face Jobs, hand off to the hugging-face-jobs skill and pass it one of the local scripts in this skill.

If the user wants to publish results into the community evals workflow, stop after generating the evaluation run and hand off that publishing step to ~/code/community-evals.

All paths below are relative to the directory containing this SKILL.md.

When To Use Which Script

Use caseScript
Local inspect-ai eval on a Hub model via inference providersscripts/inspect_eval_uv.py
Local GPU eval with inspect-ai using vllm or Transformersscripts/inspect_vllm_uv.py
Local GPU eval with lighteval using vllm or acceleratescripts/lighteval_vllm_uv.py
Extra command patternsexamples/USAGE_EXAMPLES.md

Prerequisites

  • Prefer uv run for local execution.

  • Set HF_TOKEN for gated/private models.

  • For local GPU runs, verify GPU access before starting:

uv --version
printenv HF_TOKEN >/dev/null
nvidia-smi

If nvidia-smi is unavailable, either:

  • use scripts/inspect_eval_uv.py for lighter provider-backed evaluation, or

  • hand off to the hugging-face-jobs skill if the user wants remote compute.

Core Workflow

  1. Choose the evaluation framework.

    • Use inspect-ai when you want explicit task control and inspect-native flows.

    • Use lighteval when the benchmark is naturally expressed as a lighteval task string, especially leaderboard-style tasks.

  2. Choose the inference backend.

    • Prefer vllm for throughput on supported architectures.

    • Use Hugging Face Transformers (--backend hf) or accelerate as compatibility fallbacks.

  3. Start with a smoke test.

    • inspect-ai: add --limit 10 or similar.

    • lighteval: add --max-samples 10.

  4. Scale up only after the smoke test passes.

  5. If the user wants remote execution, hand off to hugging-face-jobs with the same script + args.

Quick Start

Option A: inspect-ai with local inference providers path

Best when the model is already supported by Hugging Face Inference Providers and you want the lowest local setup overhead.

uv run scripts/inspect_eval_uv.py \
  --model meta-llama/Llama-3.2-1B \
  --task mmlu \
  --limit 20

Use this path when:

  • you want a quick local smoke test

  • you do not need direct GPU control

  • the task already exists in inspect-evals

Option B: inspect-ai on Local GPU

Best when you need to load the Hub model directly, use vllm, or fall back to Transformers for unsupported architectures.

Local GPU:

uv run scripts/inspect_vllm_uv.py \
  --model meta-llama/Llama-3.2-1B \
  --task gsm8k \
  --limit 20

Transformers fallback:

uv run scripts/inspect_vllm_uv.py \
  --model microsoft/phi-2 \
  --task mmlu \
  --backend hf \
  --trust-remote-code \
  --limit 20

Option C: lighteval on Local GPU

Best when the task is naturally expressed as a lighteval task string, especially Open LLM Leaderboard style benchmarks.

Local GPU:

uv run scripts/lighteval_vllm_uv.py \
  --model meta-llama/Llama-3.2-3B-Instruct \
  --tasks "leaderboard|mmlu|5,leaderboard|gsm8k|5" \
  --max-samples 20 \
  --use-chat-template

accelerate fallback:

uv run scripts/lighteval_vllm_uv.py \
  --model microsoft/phi-2 \
  --tasks "leaderboard|mmlu|5" \
  --backend accelerate \
  --trust-remote-code \
  --max-samples 20

Remote Execution Boundary

This skill intentionally stops at local execution and backend selection.

If the user wants to:

  • run these scripts on Hugging Face Jobs

  • pick remote hardware

  • pass secrets to remote jobs

  • schedule recurring runs

  • inspect / cancel / monitor jobs

then switch to the hugging-face-jobs skill and pass it one of these scripts plus the chosen arguments.

Task Selection

inspect-ai examples:

  • mmlu

  • gsm8k

  • hellaswag

  • arc_challenge

  • truthfulqa

  • winogrande

  • humaneval

lighteval task strings use suite|task|num_fewshot:

  • leaderboard|mmlu|5

  • leaderboard|gsm8k|5

  • leaderboard|arc_challenge|25

  • lighteval|hellaswag|0

Multiple lighteval tasks can be comma-separated in --tasks.

Backend Selection

  • Prefer inspect_vllm_uv.py --backend vllm for fast GPU inference on supported architectures.

  • Use inspect_vllm_uv.py --backend hf when vllm does not support the model.

  • Prefer lighteval_vllm_uv.py --backend vllm for throughput on supported models.

  • Use lighteval_vllm_uv.py --backend accelerate as the compatibility fallback.

  • Use inspect_eval_uv.py when Inference Providers already cover the model and you do not need direct GPU control.

Hardware Guidance

Model sizeSuggested local hardware
< 3Bconsumer GPU / Apple Silicon / small dev GPU
3B - 13Bstronger local GPU
13B+high-memory local GPU or hand off to hugging-face-jobs

For smoke tests, prefer cheaper local runs plus --limit or --max-samples.

Troubleshooting

  • CUDA or vLLM OOM:

    • reduce --batch-size

    • reduce --gpu-memory-utilization

    • switch to a smaller model for the smoke test

    • if necessary, hand off to hugging-face-jobs

  • Model unsupported by vllm:

    • switch to --backend hf for inspect-ai

    • switch to --backend accelerate for lighteval

  • Gated/private repo access fails:

    • verify HF_TOKEN

  • Custom model code required:

    • add --trust-remote-code

Examples

See:

  • examples/USAGE_EXAMPLES.md for local command patterns

  • scripts/inspect_eval_uv.py

  • scripts/inspect_vllm_uv.py

  • scripts/lighteval_vllm_uv.py

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Attribution and licence

Published by
Hugging Face
Publisher
Hugging Face (Official)
Source
huggingface/skills · skills/huggingface-community-evals · commit 25910b3
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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