huggingface-community-evals

Files (6)

• SKILL.md
• .env.example · examples/
• USAGE_EXAMPLES.md · examples/
• inspect_eval_uv.py · scripts/
• inspect_vllm_uv.py · scripts/
• lighteval_vllm_uv.py · scripts/

SKILL.md (first lines)

# 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:

```bash
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.

```bash
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:

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

Transformers fallback:

```bash
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:

```bash
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
```

From huggingface/skills, Apache-2.0.

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