NVIDIA Model Optimizer is a library that changes deep-learning models through techniques such as quantization, pruning, distillation, neural architecture search, speculative decoding, and sparsity so they can run more efficiently. Developers use it to optimize Hugging Face, PyTorch, or ONNX models and export checkpoints for inference frameworks such as SGLang, TensorRT-LLM, TensorRT, or vLLM. The catalogue entries provide skills, instructions, and a plugin for using its workflows.
Borrowing it
Nothing to install: this file belongs to NVIDIA/Model-Optimizer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NVIDIA/Model-Optimizer/main/AGENTS.mdgit clone --depth 1 https://github.com/NVIDIA/Model-OptimizerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/nvidia/model-optimizer/agents-md)<a href="https://agentmods.dev/instructions/nvidia/model-optimizer/agents-md"><img src="https://agentmods.dev/badge/instructions/nvidia/model-optimizer/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/nvidia/model-optimizer/agents-md"><img src="https://agentmods.dev/badge/instructions/nvidia/model-optimizer/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.02231 | $0.02231 |
| Opus 5.5 | $0.00892 | $0.00892 |
| Sonnet 5.5 | $0.00446 | $0.00446 |
| Haiku 4.5 | $0.00223 | $0.00223 |
Grade A, and why
Model-Optimizer AGENTS.md 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 6d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Instructions for ModelOpt
These instructions apply to AI-assisted work in this repository.
Repository orientation
- Start with
README.mdfor project overview and install. - Use
modelopt/for source,tests/for focused test coverage, andexamples/ordocs/for usage patterns. - Agent skills live under
plugins/modelopt/skills/, the installable plugin's canonical skill tree..agents/skillsand.claude/skillsexpose those skills through relative symlinks. Shared agent config and scripts remain under.agents/. See.agents/README.mdfor the convention.
Coding guidelines
- Coding guide: Code development and review require reading and following the coding standards in CONTRIBUTING.md; do not skip this step.
- Use relative paths from the repo root in commands and file references.
Iterative development
- Running tests: Follow the
writing and running tests
instructions. For fast initial iteration, choose focused tests for the
changed area from
tests/. - Running pre-commit: Follow the pre-commit hook instructions. Hooks may modify files; review and re-stage those changes before committing.
- Signed commit: Use
git commit -s -S -m "<message>"for commits so they follow the signing your work requirements. - Never
git pushwithout explicit approval in the current turn. Commit locally is fine; publishing to a remote is not. - After
git commit, stop and wait for the user to say "push", "publish", "ship", or equivalent before runninggit push,gh pr create, or any push-option flags like-o merge_request.create.
Adding tests
- Be stingy. Per Test design principles, check existing unit/GPU/example tests first and extend or parametrize rather than add. Skip tests that restate the implementation, assert on mocks or message wording, or cover a helper its caller's test already exercises. Dev scaffolding tests stay out of the commit.
- Keep
tests/unitfast. ThelinuxCI job times out at 20 min. A new unit test must run in well under 5s locally (--durations); anything spawning processes/subprocesses, multi-step training or large models goes totests/gpu*(except tests sharing the session-scopedcpu_dist_workers). In the pre-push pass, list the tests you added and why each is needed.
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.
- 6d ago Changed · +10 lines · +185 tokens per session bda618657c2c
- 9d ago Changed · +30 lines · +422 tokens per session c3c120caed39
- 19d ago Changed · +7 lines · +136 tokens per session cd15d2664697
- 20d ago Changed · +1 lines · +25 tokens per session 664ef7c02cb8
- 28d ago Changed · +9 lines · +85 tokens per session 6b544953ff63
- 29d ago Changed · +29 lines · +458 tokens per session a94c05f53052
- 1mo ago First seen · 74 lines · 920 tokens per session scan A f791a2eb9d3d
Model-Optimizer AGENTS.md is an instructions file published in the GitHub repository NVIDIA/Model-Optimizer (5,398 stars, last pushed today), licensed Apache-2.0. It adds 2,231 tokens to every session, about $0.0089 per session on Opus 5.5. 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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next.js AGENTS.md
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