huggingface-community-evals – skill Agent & Tự động hóa cho agent AI

When to Use

• Use when this upstream workflow matches the user's stated goal.
• Use when the task requires the procedures documented in this skill.

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 publication is requested, stop after producing local results; ask for the actual reviewed community-evals checkout. ~/code/community-evals is not bundled or guaranteed to exist.

> 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

The bundled lighteval wrapper is disabled pending a release-specific CLI migration: its generic version range mixes incompatible APIs. Use official documentation for a pinned release instead. Inspect examples also require matching installed provider/task versions. Provider-backed runs send prompts externally and may incur charges; agree on recipient, sample limit and budget first. For reproducibility record dependency/model/dataset revisions and generation settings. Never enable remote model code automatically; review a pinned revision and isolate it. Generated-code tasks need enforced sandbox containment; local is not isolation.

• 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: locally orchestrated Inspect with remote Inference Providers

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 \
  --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 \
  --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 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-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:
• Stop if a model requires custom remote code. These wrappers deliberately reject --trust-remote-code; use only standard model architectures here. A separate custom-code workflow must pin an immutable model revision, inspect every code file, obtain explicit approval, and run without credentials in filesystem/network isolation.

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

Examples

User: Apply this skill to my current task.
Assistant: Follow the workflow in this skill, cite limitations, and ask before risky steps.

Limitations

• Imported upstream skill; verify credentials, permissions, and safety boundaries before execution.
• Does not replace environment-specific validation, testing, or maintainer review.