mirror of
https://github.com/NVIDIA/Model-Optimizer.git
synced 2026-10-18 05:40:03 +08:00
Chad Voegele
d3fe8117ff
### What does this PR do? Type of change: new feature Packages the existing ModelOpt agent skills as installable Codex and Claude plugins: - Adds a repo-scoped Codex marketplace and Claude-compatible marketplace. - Adds the canonical `plugins/modelopt/` plugin tree and manifests. - Moves the skill tree into the plugin and keeps `.agents/skills` as a compatibility symlink. - Adds a minimal `common` placeholder skill required by Codex validation. - Documents installation from this repository. ### Usage ```bash codex plugin marketplace add NVIDIA/Model-Optimizer ``` Then open `/plugins`, select the `modelopt` marketplace, and install `modelopt`. For Claude Code: ```bash claude plugin marketplace add https://github.com/NVIDIA/Model-Optimizer.git claude plugin install modelopt@modelopt ``` ### Testing - Codex plugin validator - `claude plugin validate . --strict` - `claude plugin validate plugins/modelopt --strict` 1. Install the marketplace plugin with Codex and Claude from an unrelated temporary workspace. 2. Exercise packaged evaluation helpers, a day-0 gate, and the shared remote helper from that workspace. 3. Run `uv run --frozen --extra dev python -m pytest -q plugins/modelopt/skills/day0-release/tests/test_gates.py plugins/modelopt/skills/benchmark-model-kernels/tests`. 4. Run pre-commit hooks for all changed files. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ — added a plugin-path validator; existing focused skill tests and installed-plugin smoke tests pass. - Did you update Changelog?: N/A — agent tooling and distribution only. - Did you get Claude approval on this PR?: N/A ### Additional Information Skills remain available through `.agents/skills`; bundled helpers are packaged under the plugin and resolved from `$SKILL_DIR` so installed workflows do not depend on the current workspace. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added installable ModelOpt plugins for Claude Code and Codex. * Added skills for PTQ, deployment, evaluation, monitoring, debugging, benchmarking, MLflow access, EAGLE3 workflows, and release management. * Added deployment helpers, evaluation recipes, checkpoint validation, and release-gating tools. * **Documentation** * Expanded setup, credential, SLURM, benchmarking, deployment, evaluation, troubleshooting, and workspace guidance. * Added installation instructions and updated agent-skill discovery guidance. * **Maintenance** * Updated skill references and compatibility links for reliable use across supported plugin environments. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Chad Voegele <cvoegele@nvidia.com>
39 lines
1.1 KiB
JSON
39 lines
1.1 KiB
JSON
{
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"name": "modelopt",
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"version": "0.1.0",
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"description": "Skills for Model Optimizer development, quantization, deployment, and evaluation.",
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"author": {
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"name": "NVIDIA Corporation",
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"url": "https://github.com/NVIDIA/Model-Optimizer"
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},
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"homepage": "https://github.com/NVIDIA/Model-Optimizer",
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"repository": "https://github.com/NVIDIA/Model-Optimizer",
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"license": "Apache-2.0",
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"keywords": [
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"modelopt",
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"quantization",
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"evaluation",
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"deployment",
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"llm"
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],
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"skills": "./skills/",
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"mcpServers": "./.mcp.json",
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"interface": {
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"displayName": "ModelOpt",
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"shortDescription": "Optimize, deploy, and evaluate models.",
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"longDescription": "Provides Model Optimizer workflows for post-training quantization, deployment, evaluation, result comparison, and release validation.",
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"developerName": "NVIDIA",
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"category": "Developer Tools",
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"capabilities": [
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"Interactive",
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"Write"
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],
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"defaultPrompt": [
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"Quantize this model with ModelOpt.",
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"Deploy and evaluate this checkpoint.",
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"Compare the baseline and quantized results."
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],
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"brandColor": "#76B900"
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}
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}
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