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### What does this PR do? JIRA ticket: https://jirasw.nvidia.com/browse/OMNIML-3469 Type of change: New feature Decouple EAGLE training rope configuration from export rope configuration, enabling separate YaRN rope scaling injection at export time for long-context inference. #### Changes **Configurable export rope scaling (`EagleConfig`)** - Add `eagle_export_rope_scaling` field to `EagleConfig` with default YaRN config (`factor=32.0`, `original_max_position_embeddings=2048`) - Set to `{}` to disable rope scaling injection at export **Simplified training defaults (`default_config.py`)** - Change default training rope from `llama3` (theta=500k) to `default` (theta=10k) — models now train with simple positional embeddings; rope scaling is applied only at export - Add `rope_theta` inside `rope_scaling` dict for transformers 5.x cross-version compatibility **Move config validation/rewriting into `EagleConfig` (`config.py`)** - `_derive_eagle_offline`: derives `eagle_offline` from `data_args.offline_data_path` via validation context, removing manual assignment in `main.py` - `_check_rope_scaling_consistency`: rejects configs where `eagle_export_rope_scaling` is set but training `rope_type` is not `"default"` - `_warn_rope_vs_training_seq_len`: warns when `original_max_position_embeddings` differs from `training_seq_len` **Export rope injection (`hf_spec_export.py`)** - Inject `eagle_export_rope_scaling` into the exported HF config when training rope_type is `"default"` - Fall back `rope_theta` from `rope_scaling` dict for transformers 5.x compatibility **Fix Megatron RotaryEmbedding crash (`megatron_eagle.py`)** - `dict_to_config()` set `rope_scaling=True` whenever the `rope_scaling` key existed, even without a `"factor"` — causing `RotaryEmbedding` to divide by `None` - Now only enables `rope_scaling` when the dict actually contains a `"factor"` key ### Usage Configure in YAML config (or use defaults from `eagle3.yaml`): ```yaml eagle: eagle_export_rope_scaling: rope_type: yarn factor: 32.0 original_max_position_embeddings: 2048 ``` Set to empty dict to disable export rope injection: ```yaml eagle: eagle_export_rope_scaling: {} ``` ### Testing - New unit tests: `tests/unit/torch/speculative/test_eagle_config.py` — rope consistency validator, seq_len warning, context-derived `eagle_offline` - New unit tests: `tests/unit/torch/export/test_hf_spec_rope_export.py` — export rope injection, fallback, and empty-config cases ### 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?: ✅ (new field has sensible default; existing configs work unchanged) - 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?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ❌ (should be added if merging as a feature) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Add export-time rope-scaling configuration for EAGLE models. * **Improvements** * Stronger validation and context-aware reconciliation between training and export configs. * Export now injects rope-scaling and rope-theta when appropriate. * Default rope-scaling values updated for EAGLE variants. * Model instances now expose export rope-scaling for downstream use. * **Tests** * Added unit tests covering rope-scaling export behavior and configuration validators. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com>
NVIDIA Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models.
[Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model.
[Optimize] Model Optimizer provides Python APIs for users to easily compose the above model optimization techniques and export an optimized quantized checkpoint. Model Optimizer is also integrated with NVIDIA Megatron-Bridge, Megatron-LM and Hugging Face Accelerate for training required inference optimization techniques.
[Export for deployment] Seamlessly integrated within the NVIDIA AI software ecosystem, the quantized checkpoint generated from Model Optimizer is ready for deployment in downstream inference frameworks like SGLang, TensorRT-LLM, TensorRT, or vLLM. The unified Hugging Face export API now supports both transformers and diffusers models.
Latest News
- [2026/03/11] Model Optimizer quantized Nemotron-3-Super checkpoints are available on Hugging Face for download: FP8, NVFP4. Learn more in the Nemotron 3 Super release blog. Check out how to quantize Nemotron 3 models for deployment acceleration here
- [2026/03/11] NeMo Megatron Bridge now supports Nemotron-3-Super quantization (PTQ and QAT) and export workflows using the Model Optimizer library. See the Quantization (PTQ and QAT) guide for FP8/NVFP4 quantization and HF export instructions.
- [2025/12/11] BLOG: Top 5 AI Model Optimization Techniques for Faster, Smarter Inference
- [2025/12/08] NVIDIA TensorRT Model Optimizer is now officially rebranded as NVIDIA Model Optimizer.
- [2025/10/07] BLOG: Pruning and Distilling LLMs Using NVIDIA Model Optimizer
- [2025/09/17] BLOG: An Introduction to Speculative Decoding for Reducing Latency in AI Inference
- [2025/09/11] BLOG: How Quantization Aware Training Enables Low-Precision Accuracy Recovery
- [2025/08/29] BLOG: Fine-Tuning gpt-oss for Accuracy and Performance with Quantization Aware Training
- [2025/08/01] BLOG: Optimizing LLMs for Performance and Accuracy with Post-Training Quantization
- [2025/06/24] BLOG: Introducing NVFP4 for Efficient and Accurate Low-Precision Inference
- [2025/05/14] NVIDIA TensorRT Unlocks FP4 Image Generation for NVIDIA Blackwell GeForce RTX 50 Series GPUs
- [2025/04/21] Adobe optimized deployment using Model-Optimizer + TensorRT leading to a 60% reduction in diffusion latency, a 40% reduction in total cost of ownership
- [2025/04/05] NVIDIA Accelerates Inference on Meta Llama 4 Scout and Maverick. Check out how to quantize Llama4 for deployment acceleration here
- [2025/03/18] World's Fastest DeepSeek-R1 Inference with Blackwell FP4 & Increasing Image Generation Efficiency on Blackwell
- [2025/02/25] Model Optimizer quantized NVFP4 models available on Hugging Face for download: DeepSeek-R1-FP4, Llama-3.3-70B-Instruct-FP4, Llama-3.1-405B-Instruct-FP4
- [2025/01/28] Model Optimizer has added support for NVFP4. Check out an example of NVFP4 PTQ here.
- [2025/01/28] Model Optimizer is now open source!
Previous News
- [2024/10/23] Model Optimizer quantized FP8 Llama-3.1 Instruct models available on Hugging Face for download: 8B, 70B, 405B.
- [2024/09/10] Post-Training Quantization of LLMs with NVIDIA NeMo and Model Optimizer.
- [2024/08/28] Boosting Llama 3.1 405B Performance up to 44% with Model Optimizer on NVIDIA H200 GPUs
- [2024/08/28] Up to 1.9X Higher Llama 3.1 Performance with Medusa
- [2024/08/15] New features in recent releases: Cache Diffusion, QLoRA workflow with NVIDIA NeMo, and more. Check out our blog for details.
- [2024/06/03] Model Optimizer now has an experimental feature to deploy to vLLM as part of our effort to support popular deployment frameworks. Check out the workflow here
- [2024/05/08] Announcement: Model Optimizer Now Formally Available to Further Accelerate GenAI Inference Performance
- [2024/03/27] Model Optimizer supercharges TensorRT-LLM to set MLPerf LLM inference records
- [2024/03/18] GTC Session: Optimize Generative AI Inference with Quantization in TensorRT-LLM and TensorRT
- [2024/03/07] Model Optimizer's 8-bit Post-Training Quantization enables TensorRT to accelerate Stable Diffusion to nearly 2x faster
- [2024/02/01] Speed up inference with Model Optimizer quantization techniques in TRT-LLM
Install
To install stable release packages for Model Optimizer with pip from PyPI:
pip install -U nvidia-modelopt[all]
To install from source in editable mode with all development dependencies or to use the latest features, run:
# Clone the Model Optimizer repository
git clone git@github.com:NVIDIA/Model-Optimizer.git
cd Model-Optimizer
pip install -e .[dev]
You can also directly use the TensorRT-LLM docker images
(e.g., nvcr.io/nvidia/tensorrt-llm/release:<version>), which have Model Optimizer pre-installed.
Make sure to upgrade Model Optimizer to the latest version as described above.
Visit our installation guide for
more fine-grained control on installed dependencies or for alternative docker images and environment variables to setup.
Techniques
| Technique | Description | Examples | Docs |
|---|---|---|---|
| Post Training Quantization | Compress model size by 2x-4x, speeding up inference while preserving model quality! | [LLMs] [diffusers] [VLMs] [onnx] [windows] | [docs] |
| Quantization Aware Training | Refine accuracy even further with a few training steps! | [Hugging Face] | [docs] |
| Pruning | Reduce your model size and accelerate inference by removing unnecessary weights! | [General] [Megatron-Bridge] | |
| Distillation | Reduce deployment model size by teaching small models to behave like larger models! | [Megatron-Bridge] [Megatron-LM] [Hugging Face] | [docs] |
| Speculative Decoding | Train draft modules to predict extra tokens during inference! | [Megatron] [Hugging Face] | [docs] |
| Sparsity | Efficiently compress your model by storing only its non-zero parameter values and their locations | [PyTorch] | [docs] |
Pre-Quantized Checkpoints
- Ready-to-deploy checkpoints [🤗 Hugging Face - Nvidia Model Optimizer Collection]
- Deployable on TensorRT-LLM, vLLM and SGLang
- More models coming soon!
Resources
Model Support Matrix
| Model Type | Support Matrix |
|---|---|
| LLM Quantization | View Support Matrix |
| Diffusers Quantization | View Support Matrix |
| VLM Quantization | View Support Matrix |
| ONNX Quantization | View Support Matrix |
| Windows Quantization | View Support Matrix |
| Quantization Aware Training | View Support Matrix |
| Pruning | View Support Matrix |
| Distillation | View Support Matrix |
| Speculative Decoding | View Support Matrix |
Contributing
Model Optimizer is now open source! We welcome any feedback, feature requests and PRs. Please read our Contributing guidelines for details on how to contribute to this project.
Top Contributors
Happy optimizing!