Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nvidia/model-optimizer/qadnpx skills add NVIDIA/Model-Optimizer --skill qadgit clone --depth 1 https://github.com/NVIDIA/Model-OptimizerWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00069 | $0.01332 |
| Opus 5 | $0.00034 | $0.00666 |
| Sonnet 5 | $0.00014 | $0.00266 |
| Haiku 4.5 | $0.00007 | $0.00133 |
Grade A, and why
qad scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 usageexamples/megatron_bridge/{quantize.py,distill.py}via--help- the common skill's
environment-setup.md,workspace-management.md, andslurm-setup.md; also itsremote-execution.mdfor 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
- 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%.
- Reproduce PTQ and verify compatibility. In the target runtime, require
AutoBridge.can_handle()for the target model and PTQ throughquantize.pyto 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.quant_summary.txt, require finite positiveamaxfor enabled static quantizers; acceptdynamic/format-definedNoneonly when the recipe intends it. Treat the summary as rank-local under model parallelism. - Choose topology explicitly. Derive the smallest fitting node count and
TP/PP/CP/EP from student and teacher architecture, the chosen sequence length,
and available GPU memory. Prefer CP before TP for small long-context models;
keep EP=1 for dense models and ETP=1 because the current
distill.pyworkflow does not support expert tensor parallelism. For MoE require:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 107 lines · 69 tokens per session scan A 53854abd981a
qad is a skill published in the GitHub repository NVIDIA/Model-Optimizer (3,612 stars, last pushed 2d ago), licensed Apache-2.0. It adds 69 tokens to every session and 1,332 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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