qad

name	qad
description	Run explicitly requested ModelOpt Quantization-Aware Distillation (QAD) on Slurm through Megatron Bridge to recover a measured BF16-to-PTQ accuracy gap. Use only when the user explicitly asks for QAD, including its topology, data preparation, Slurm launch, resume, checkpoint export, or recovery decisions.

ModelOpt Quantization-Aware Distillation

QAD is expensive. Run it only when the user explicitly authorizes QAD for the target model or run. A Day-0, PTQ, evaluation, comparison, or recipe-search request alone is not authorization to start QAD.

Follow the supported workflow

Before constructing commands, read:

• examples/megatron_bridge/README.md, especially PTQ, data preparation, QAD, export, and Slurm usage
• examples/megatron_bridge/{quantize.py,distill.py} via --help
• the common skill's environment-setup.md, workspace-management.md, and slurm-setup.md; also its remote-execution.md for remote Slurm

Treat the example README and --help output as authoritative for mutable flags, commands, containers, and checkpoint formats. This skill supports Slurm only.

Execute in this order

1. Confirm the gap. Reuse only validated, comparable BF16/PTQ results and the exact benchmark configuration from preceding evaluation or recipe search; run missing, invalid, or non-comparable baselines. Confirm the target benchmarks and their context-length needs. Stop if the PTQ gap to BF16 is already below 1%.

2. Reproduce PTQ and verify compatibility. In the target runtime, require AutoBridge.can_handle() for the target model and PTQ through quantize.py to succeed while preserving the exact preceding PTQ config or recipe: format, layer selection, calibration data/count, sequence length, and seed. A changed quantization setting is a new PTQ candidate and must be evaluated before QAD. In the master-rank , require finite positive for enabled static quantizers; accept /format-defined only when the recipe intends it. Treat the summary as rank-local under model parallelism.