--- name: huggingface-community-evals-a97f1c703f55 description: Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation. --- # 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 ``` `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` 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: - 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` --- Source attribution: pinkpixel-dev Original project: https://github.com/pinkpixel-dev/skills-collection-1/blob/dc709653773653302e333a45d3b6f3b70fdff8f8/SKILLS/huggingface-community-evals/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: pinkpixel-dev Original project: https://github.com/pinkpixel-dev/skills-collection-1/blob/dc709653773653302e333a45d3b6f3b70fdff8f8/SKILLS/huggingface-community-evals/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Backend Developer, Data Scientist, ML / AI Engineer Specialties: Backend Tools: Inspect Ai, Lighteval Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: c743541407dffd247c1cdf72b36842e452ee2c271fa383a1894995b168d26ee8