Runs local GPU evaluations of Hugging Face Hub models with inspect-ai or lighteval and vLLM, Transformers, or accelerate backends (uv scripts). Use when choosing an eval framework, running a smoke test, or picking a local inference backend. Not for remote Hugging Face Jobs, model-card or model-index edits (hugging-face-evaluation), or publishing .eval_results.
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SKILL.md
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name
hugging-face-community-evals
description
Runs local GPU evaluations of Hugging Face Hub models with inspect-ai or lighteval and vLLM, Transformers, or accelerate backends (uv scripts). Use when choosing an eval framework, running a smoke test, or picking a local inference backend. Not for remote Hugging Face Jobs, model-card or model-index edits (hugging-face-evaluation), or publishing .eval_results.
This skill runs evaluations against models hosted on the Hugging Face Hub using local hardware. It covers two evaluation frameworks—inspect-ai and lighteval—and three inference backends: vllm, Hugging Face Transformers (hf), and accelerate.
It does not cover:
Hugging Face Jobs orchestration (hand off to hugging-face-jobs)
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 from 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
User wants to evaluate a Hugging Face Hub model on local GPU hardware
User needs to choose between inspect-ai and lighteval
User needs to choose between vllm, Transformers, or accelerate backends
User wants to run a quick smoke test before scaling up
User is doing backend selection or local GPU evals
Do not use this skill for: HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
Prerequisites
Install uv (preferred runner for all scripts in this skill).
Set HF_TOKEN environment variable for gated or private models. Use YOUR_KEY as placeholder—never hardcode live secrets.
For local GPU runs, verify GPU access before starting:
uv --version
$env:HF_TOKEN # Should be set; verify it exists
nvidia-smi
If nvidia-smi is unavailable on Windows:
Use scripts/inspect_eval_uv.py for lighter provider-backed evaluation (no local GPU needed), or
Hand off to the hugging-face-jobs skill if the user wants remote compute.
Procedure
Step 1: Choose the Evaluation Framework
Framework
When to choose
inspect-ai
You want explicit task control and inspect-native flows
lighteval
The benchmark is naturally expressed as a lighteval task string (leaderboard-style tasks)
Step 2: Choose the Inference Backend
Backend
When to choose
vllm
Throughput on supported architectures (preferred)
hf (Transformers)
vllm does not support the model (inspect-ai fallback)
accelerate
vllm does not support the model (lighteval fallback)
Inference Providers
No direct GPU control needed; model already supported by HF Inference Providers
Step 3: Select the Script
Use case
Script
Local inspect-ai eval 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
Load examples/USAGE_EXAMPLES.md when the user asks for additional command patterns beyond the quick-start options below.
Step 4: Run a Smoke Test First
Always start with a limited sample size:
inspect-ai: add --limit 10 (or similar small number)
lighteval: add --max-samples 10
Step 5: Scale Up
Only after the smoke test passes, remove the limit flag or increase it to the full dataset.
Step 6: Remote Handoff (if needed)
If the user wants remote execution, hand off to hugging-face-jobs with the same script and arguments.
Quick Start Commands
Option A: inspect-ai with Inference Providers (no local GPU needed)
Best when the model is already supported by Hugging Face Inference Providers and you want the lowest local setup overhead.
Multiple lighteval tasks can be comma-separated in --tasks.
Backend Selection Summary
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 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.
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.
Pitfalls
CUDA or vLLM OOM: Reduce --batch-size, reduce --gpu-memory-utilization, switch to a smaller model for the smoke test, or hand off to hugging-face-jobs.
Model unsupported by vllm: Switch to --backend hf for inspect-ai, or --backend accelerate for lighteval.
Gated/private repo access fails: Verify HF_TOKEN is set and valid. Use YOUR_KEY as placeholder in examples—never commit live tokens.
Custom model code required: Add --trust-remote-code flag.
Skipping smoke test: Always run with --limit or --max-samples first. Full runs on large datasets can consume significant GPU time and memory.
Wrong framework for task type: lighteval is better for leaderboard-style benchmarks expressed as task strings. inspect-ai is better for explicit task control and custom eval flows.
Assuming remote execution: This skill is local-only. Do not attempt to orchestrate Hugging Face Jobs from these scripts.
Verification
Verify uv is installed:
uv --version
Expected: prints a version number (e.g., uv 0.x.x).
Verify HF_TOKEN is set (for gated models):
if ($env:HF_TOKEN) { "HF_TOKEN is set" } else { "HF_TOKEN is NOT set" }
Expected: HF_TOKEN is set
Verify GPU access (for local GPU scripts):
nvidia-smi
Expected: GPU information table with memory and utilization stats.
