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name: genai-lab description: "Run and configure AIMET GenAILab LLM/VLM quantization scorecards with Torch or ONNX backends, exports, caches, and summaries." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

AIMET GenAILab

Use this sub-skill when the user asks about AIMET GenAI scorecards, python -m GenAILab, LLM/VLM quantization recipes, YAML configs, recipe caches, FP/model caches, exported GenAI artifacts, local Torch/ONNX GenAI runs, online GitHub Actions scorecards, or result summaries.

Read/run first

• Read GenAILab workflows for local, online, export, cache, and result-summary flows.
• Read model access and credentialed evaluation when the model or metric needs Hugging Face, GitHub CLI, AWS/S3, SAML, or external benchmark data.
• Read backend compatibility before claiming CUDA, ONNX Runtime CUDA provider, or large-model capacity.
• Run genai_config_preflight.py before launching a local or online run; it validates YAML shape without downloading models or datasets.
• Run genai_results_summary.py on profiling_data.json when you only need to inspect results or detect mixed metric scoring versions.

Core workflow

1. Preflight the config. Validate required model, metrics, precision, recipe, datasets, and adaptations before allocating GPU time.
2. Choose framework. Use torch for AIMET Torch recipes, onnx for ONNX QuantSim/Runtime recipes, or both to run both sequentially locally.
3. Plan credentials and caches. Hugging Face access is needed for gated or remote models/datasets. Use explicit cache directories for FP outputs, recipe checkpoints, and ONNX model exports when runs are expensive.
4. Run locally when the environment is ready. Use python -m GenAILab --framework <torch|onnx|both> --config cfg.yaml plus output/cache flags.
5. Run online only when GitHub credentials and pushed code are appropriate. --online dispatches GitHub Actions and uses the last pushed commit, not uncommitted local files.
6. Inspect outputs. Results append to profiling JSON/CSV, while exports contain tokenizer/config, ONNX/encodings, and optional secondary ONNX-eval artifacts.

Decision points

• Model size and context length: LLM recipes can require tens of GB of VRAM; validate sequence/context length and precision before launch.
• Metrics: PPL uses Wikitext; MMLU/MMMU-style metrics require benchmark datasets and scoring-version compatibility.
• Recipe chain: A chain that does not end with Calibration, RemoveQuantization, or Skip has calibration auto-inserted by the parser.
• Export: export: true or eval_in_onnx: true creates artifacts under the selected export root. Use the export inspector and SDK sub-skill before target handoff.

Boundaries

• Route pod launch/sync/stop work to cluster-pod-workflows.
• Route S3 checkpoint downloads, Hugging Face tokens, GitHub Actions download/merge, and result comparability to model-access-and-credentialed-evaluation.
• Route QNN/QAIRT/AI Hub deployment from exported artifacts to qualcomm-sdk-deployment.
• Route ordinary CNN/ResNet/MobileNet quantization outside GenAILab to the Torch/ONNX sub-skills.

Expected answer shape

For a GenAILab task, include the config document or patch, the framework, local/online decision, credential requirements, cache/export/results directories, expected outputs, and a bounded validation command before starting long runs.