huggingface-community-evals

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 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 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: