mirror of
https://github.com/NVIDIA/Model-Optimizer.git
synced 2026-10-02 03:14:52 +08:00
### What does this PR do? Type of change: ? New example <!-- Details about the change. --> Adds example for Alpamayo-1 quantization with ModelOpt (FP8, NVFP4, AutoQuant) ### Usage ``` python quantize.py --ckpt nvidia/Alpamayo-R1-10B --output-dir ./alpamayo-r1-fp8 --quantize fp8 ``` ### Testing <!-- Mention how have you tested your change if applicable. --> ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ / ❌ / N/A <!--- If ❌, explain why. --> - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ / ❌ / N/A <!--- Mandatory --> - Did you write any new necessary tests?: ✅ / ❌ / N/A <!--- Mandatory for new features or examples. --> - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ / ❌ / N/A <!--- Only for new features, API changes, critical bug fixes or backward incompatible changes. --> - Did you get Claude approval on this PR?: ✅ / ❌ / N/A <!--- Run `/claude review`. NVIDIA org members can self-trigger for complex changes; orthogonal to CodeRabbit. --> ### Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added Alpamayo 1 vision-language-action model quantization example supporting FP8, NVFP4, and mixed-precision optimization modes * Introduced CLI quantization tool with calibration loop and checkpoint export capabilities for both fake-quantized and real-quantized formats * **Documentation** * Added comprehensive guide documenting the Alpamayo quantization example, model details, and usage instructions <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Rohan Joshi <rohjoshi@nvidia.com>