Install any skill in seconds. Free to start, no credit card required.
Get Started Free →lingxling/hugging-face-community-evals
Run local evaluations for Hugging Face Hub models with inspect-ai or lighteval.
Passed 19 of 20 test cases with the skill, and 3 of 20 without it.
and 20 more in the full report
18 fixed3 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
When conducting an initial smoke test for lighteval on a local GPU before scaling up full benchmarks, what command argument limits evaluation to 10 samples?
| Checked | Without skill | With skill |
|---|---|---|
| Command specifies --max-samples 10 | Missed | Passed |
The instructions your model reads when the skill loads.
Use this skill for local model evaluation, backend selection, and GPU smoke tests outside the Hugging Face Jobs workflow.
This skill is for running evaluations against models on the Hugging Face Hub on local hardware.
It covers:
inspect-ai with local inferencelighteval with local inferencevllm, Hugging Face Transformers, and accelerateIt does not cover:
model-index edits.eval_results generation or publishingIf 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.
| 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 |
uv run for local execution.HF_TOKEN for gated/private models.test cases passed, vs 3 / 20 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.