Huggingface Community Evals
Hugging FaceOfficialPublished by Hugging Face
- Skill
- Tool integration
- Software development
- Apache-2.0
- Low risk
- Has scripts
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/huggingface/huggingface-community-evals/downloadUnzip 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-aiwith local inferencelightevalwith local inferencechoosing between
vllm, Hugging Face Transformers, andacceleratesmoke tests, task selection, and backend fallback strategy
It does not cover:
Hugging Face Jobs orchestration
model-card or
model-indexeditsREADME table extraction
Artificial Analysis imports
.eval_resultsgeneration or publishingPR 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 case | Script |
|---|---|
Local inspect-ai eval on a Hub model via inference providers | scripts/inspect_eval_uv.py |
Local GPU eval with inspect-ai using vllm or Transformers | scripts/inspect_vllm_uv.py |
Local GPU eval with lighteval using vllm or accelerate | scripts/lighteval_vllm_uv.py |
| Extra command patterns | examples/USAGE_EXAMPLES.md |
Prerequisites
Prefer
uv runfor local execution.Set
HF_TOKENfor gated/private models.For local GPU runs, verify GPU access before starting:
uv --version
printenv HF_TOKEN >/dev/null
nvidia-smiIf nvidia-smi is unavailable, either:
use
scripts/inspect_eval_uv.pyfor lighter provider-backed evaluation, orhand off to the
hugging-face-jobsskill if the user wants remote compute.
Core Workflow
Choose the evaluation framework.
Use
inspect-aiwhen you want explicit task control and inspect-native flows.Use
lightevalwhen the benchmark is naturally expressed as a lighteval task string, especially leaderboard-style tasks.
Choose the inference backend.
Prefer
vllmfor throughput on supported architectures.Use Hugging Face Transformers (
--backend hf) oraccelerateas compatibility fallbacks.
Start with a smoke test.
inspect-ai: add--limit 10or similar.lighteval: add--max-samples 10.
Scale up only after the smoke test passes.
If the user wants remote execution, hand off to
hugging-face-jobswith 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 20Use 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 20Transformers fallback:
uv run scripts/inspect_vllm_uv.py \
--model microsoft/phi-2 \
--task mmlu \
--backend hf \
--trust-remote-code \
--limit 20Option 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-templateaccelerate fallback:
uv run scripts/lighteval_vllm_uv.py \
--model microsoft/phi-2 \
--tasks "leaderboard|mmlu|5" \
--backend accelerate \
--trust-remote-code \
--max-samples 20Remote 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:
mmlugsm8khellaswagarc_challengetruthfulqawinograndehumaneval
lighteval task strings use suite|task|num_fewshot:
leaderboard|mmlu|5leaderboard|gsm8k|5leaderboard|arc_challenge|25lighteval|hellaswag|0
Multiple lighteval tasks can be comma-separated in --tasks.
Backend Selection
Prefer
inspect_vllm_uv.py --backend vllmfor fast GPU inference on supported architectures.Use
inspect_vllm_uv.py --backend hfwhenvllmdoes not support the model.Prefer
lighteval_vllm_uv.py --backend vllmfor throughput on supported models.Use
lighteval_vllm_uv.py --backend accelerateas the compatibility fallback.Use
inspect_eval_uv.pywhen Inference Providers already cover the model and you do not need direct GPU control.
Hardware Guidance
| Model size | Suggested local hardware |
|---|---|
< 3B | consumer GPU / Apple Silicon / small dev GPU |
3B - 13B | stronger 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-sizereduce
--gpu-memory-utilizationswitch to a smaller model for the smoke test
if necessary, hand off to
hugging-face-jobs
Model unsupported by
vllm:switch to
--backend hfforinspect-aiswitch to
--backend accelerateforlighteval
Gated/private repo access fails:
verify
HF_TOKEN
Custom model code required:
add
--trust-remote-code
Examples
See:
examples/USAGE_EXAMPLES.mdfor local command patternsscripts/inspect_eval_uv.pyscripts/inspect_vllm_uv.pyscripts/lighteval_vllm_uv.py
Files
- ATTRIBUTION.mdAdded by AutoGineer898 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.4 KiB
- examples/.env.example159 B
- examples/USAGE_EXAMPLES.md2.0 KiB
- scripts/inspect_eval_uv.pyScript2.9 KiB
- scripts/inspect_vllm_uv.pyScript8.9 KiB
- scripts/lighteval_vllm_uv.pyScript9.0 KiB
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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
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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)
- Synced
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