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Model-Optimizer/tests/examples/speculative_decoding
yeyu-nvidiaandClaude Sonnet 4.6 cccfded8a9 Add support for offline speculative decoding model PTQ (#883)
## What does this PR do?

**Type of change:**
new feature

**Overview:** 
This PR enables loading in a ModelOpt pretrained offline speculative
decoding model (e.g., EAGLE3) and performs PTQ on it and export.

## Usage
Follow the speculative_decoding examples to train an offline speculative
decoding model first.
Then follow the command below to quantize and export it:

```bash
python hf_ptq.py --pyt_ckpt_path <dir_of_offline_specdec_model> --specdec_offline_dataset <dir_of_dataset>
```

## Testing
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- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
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## Additional Information
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **New Features**
* Offline speculative decoding workflow: support loading a local dataset
for calibration, generation, and export; new CLI option to specify the
offline dataset.

* **Improvements**
* Export and quantization paths now accept and propagate offline
speculative-decoding inputs.
* Offline data loading honors a sample-size limit and enforces safe
batch sizing for calibration.

* **Bug Fixes**
* Better handling of model/config mismatches and varied batch types in
offline flows.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Ye Yu <yeyu@nvidia.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-08 23:56:45 +00:00
..
2026-04-08 02:54:18 -07:00