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

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

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.
Choose the inference backend.
- Prefer
```
vllm
```
  for throughput on supported architectures.
- Use Hugging Face Transformers (
```
--backend hf
```
  ) or
```
accelerate
```
  as compatibility fallbacks.
Start with a smoke test.
- ```
inspect-ai
```
  : add
```
--limit 10
```
  or 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-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

accelerate

fallback:

bash

uv run scripts/lighteval_vllm_uv.py \
  --model microsoft/phi-2 \
  --tasks "leaderboard|mmlu|5" \
  --backend accelerate \
  --trust-remote-code \
  --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

--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:
- add
```
--trust-remote-code
```

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
```

huggingface-community-evals

NPX Install

Tags

SKILL.md Content

Overview

When To Use Which Script

Prerequisites

Core Workflow

Quick Start

Option A: inspect-ai with local inference providers path

Option B: inspect-ai on Local GPU

Option C: lighteval on Local GPU

Remote Execution Boundary

Task Selection

Backend Selection

Hardware Guidance

Troubleshooting

Examples