Product Rename: TensorRT Model Optimizer to Model Optimizer

Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>

Author: kevalmorabia97

.github/ISSUE_TEMPLATE/1_bug_report.md

@@ -6,7 +6,7 @@ labels: bug
 assignees: ''
 ---
 
-**Before submitting an issue, please make sure it hasn't been already addressed by searching through the [existing and past issues](https://github.com/NVIDIA/TensorRT-Model-Optimizer/issues?q=is%3Aissue).**
+**Before submitting an issue, please make sure it hasn't been already addressed by searching through the [existing and past issues](https://github.com/NVIDIA/Model-Optimizer/issues?q=is%3Aissue).**
 
 ## Describe the bug
 <!-- Description of what the bug is, its impact (blocker, should have, nice to have) and any stack traces or error messages. -->
@@ -30,7 +30,7 @@ If you are unsure about whom to tag, you can leave it blank, and we will make su
 
 ## System information
 
-<!-- Run this script to automatically collect system information: https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/.github/ISSUE_TEMPLATE/get_system_info.py -->
+<!-- Run this script to automatically collect system information: https://github.com/NVIDIA/Model-Optimizer/blob/main/.github/ISSUE_TEMPLATE/get_system_info.py -->
 
 - Container used (if applicable): ?
 - OS (e.g., Ubuntu 22.04, CentOS 7, Windows 10): ? <!-- If Windows, please add the `windows` label to the issue. -->

.github/ISSUE_TEMPLATE/3_question.md

@@ -6,7 +6,7 @@ labels: question
 assignees: ''
 ---
 
-Make sure you already checked the [examples](https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples) and [documentation](https://nvidia.github.io/TensorRT-Model-Optimizer/) before submitting an issue.
+Make sure you already checked the [examples](https://github.com/NVIDIA/Model-Optimizer/tree/main/examples) and [documentation](https://nvidia.github.io/Model-Optimizer/) before submitting an issue.
 
 ## How would you like to use ModelOpt
 
@@ -23,7 +23,7 @@ If you are unsure about whom to tag, you can leave it blank, and we will make su
 
 ## System information
 
-<!-- Run this script to automatically collect system information: https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/.github/ISSUE_TEMPLATE/get_system_info.py -->
+<!-- Run this script to automatically collect system information: https://github.com/NVIDIA/Model-Optimizer/blob/main/.github/ISSUE_TEMPLATE/get_system_info.py -->
 
 - Container used (if applicable): ?
 - OS (e.g., Ubuntu 22.04, CentOS 7, Windows 10): ? <!-- If Windows, please add the `windows` label to the issue. -->

.github/PULL_REQUEST_TEMPLATE.md

@@ -17,11 +17,11 @@
 ## Before your PR is "*Ready for review*"
 <!-- If you haven't finished some of the above items you can still open `Draft` PR. -->
 
-- **Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CONTRIBUTING.md)** and your commits are signed.
+- **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/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**: Yes/No <!--- Only for new features, API changes, critical bug fixes or bw breaking changes. -->
+- **Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**: Yes/No <!--- Only for new features, API changes, critical bug fixes or bw breaking changes. -->
 
 ## Additional Information
 <!-- E.g. related issue. -->

CHANGELOG-Windows.rst

@@ -1,34 +1,33 @@
-===================================
-Model Optimizer Changelog (Windows)
-===================================
+NVIDIA Model Optimizer Changelog (Windows)
+==========================================
 
 0.33 (2025-07-21)
 ^^^^^^^^^^^^^^^^^
 
 **New Features**
 
-- TensorRT Model Optimizer for Windows now supports `NvTensorRtRtx <https://onnxruntime.ai/docs/execution-providers/TensorRTRTX-ExecutionProvider.html>`_ execution-provider.
+- Model Optimizer for Windows now supports `NvTensorRtRtx <https://onnxruntime.ai/docs/execution-providers/TensorRTRTX-ExecutionProvider.html>`_ execution-provider.
 
 
 0.27 (2025-04-30)
 ^^^^^^^^^^^^^^^^^
 
 **New Features**
 
-- New LLM models like DeepSeek etc. are supported with ONNX INT4 AWQ quantization on Windows. Refer `Windows Support Matrix <https://nvidia.github.io/TensorRT-Model-Optimizer/guides/0_support_matrix.html>`_ for details about supported features and models.
-- TensorRT Model Optimizer for Windows now supports ONNX INT8 and FP8 quantization (W8A8) of SAM2 and Whisper models. Check `example scripts <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/windows/onnx_ptq>`_ for getting started with quantizing these models.
+- New LLM models like DeepSeek etc. are supported with ONNX INT4 AWQ quantization on Windows. Refer `Windows Support Matrix <https://nvidia.github.io/Model-Optimizer/guides/0_support_matrix.html>`_ for details about supported features and models.
+- Model Optimizer for Windows now supports ONNX INT8 and FP8 quantization (W8A8) of SAM2 and Whisper models. Check `example scripts <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/windows/onnx_ptq>`_ for getting started with quantizing these models.
 
 
 0.19 (2024-11-18)
 ^^^^^^^^^^^^^^^^^
 
 **New Features**
 
-- This is the first official release of TensorRT Model Optimizer for Windows
+- This is the first official release of Model Optimizer for Windows
 - **ONNX INT4 Quantization:** :meth:`modelopt.onnx.quantization.quantize_int4 <modelopt.onnx.quantization.int4.quantize>` now supports ONNX INT4 quantization for DirectML and TensorRT* deployment. See :ref:`Support_Matrix` for details about supported features and models.
-- **LLM Quantization with Olive:** Enabled LLM quantization through Olive, streamlining model optimization workflows. Refer `example <https://github.com/microsoft/Olive/tree/main/examples/phi3#quantize-models-with-nvidia-tensorrt-model-optimizer>`_
+- **LLM Quantization with Olive:** Enabled LLM quantization through Olive, streamlining model optimization workflows. Refer `example <https://github.com/microsoft/Olive/tree/main/examples/phi3#quantize-models-with-nvidia-Model-Optimizer>`_
 - **DirectML Deployment Guide:** Added DML deployment guide. Refer :ref:`DirectML_Deployment`.
-- **MMLU Benchmark for Accuracy Evaluations:** Introduced `MMLU benchmarking <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/windows/accuracy_benchmark/README.md>`_ for accuracy evaluation of ONNX models on DirectML (DML).
+- **MMLU Benchmark for Accuracy Evaluations:** Introduced `MMLU benchmarking <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/windows/accuracy_benchmark/README.md>`_ for accuracy evaluation of ONNX models on DirectML (DML).
 - **Published quantized ONNX models collection:** Published quantized ONNX models at HuggingFace `NVIDIA collections <https://huggingface.co/collections/nvidia/optimized-onnx-models-for-nvidia-rtx-gpus>`_.
 
 

CHANGELOG.rst

@@ -1,5 +1,5 @@
-Model Optimizer Changelog (Linux)
-=================================
+NVIDIA Model Optimizer Changelog (Linux)
+========================================
 
 0.40 (2025-12-11)
 ^^^^^^^^^^^^^^^^^
@@ -12,7 +12,7 @@ Model Optimizer Changelog (Linux)
 **New Features**
 
 - Add MoE (e.g. Qwen3-30B-A3B, gpt-oss-20b) pruning support for ``num_moe_experts``, ``moe_ffn_hidden_size`` and ``moe_shared_expert_intermediate_size`` parameters in Minitron pruning (``mcore_minitron``).
-- Add ``specdec_bench`` example to benchmark speculative decoding performance. See `examples/specdec_bench/README.md <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/specdec_bench#speculative-decoding-benchmark>`_ for more details.
+- Add ``specdec_bench`` example to benchmark speculative decoding performance. See `examples/specdec_bench/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/specdec_bench#speculative-decoding-benchmark>`_ for more details.
 - Add FP8/NVFP4 KV cache quantization support for Megatron Core models.
 - Add flag ``trt_plugins_precision`` in ONNX autocast to indicate custom ops precision. This is similar to the flag already existing in the quantization workflow.
 - Add support for PyTorch Geometric quantization.
@@ -30,7 +30,7 @@ Model Optimizer Changelog (Linux)
 
 **Deprecations**
 
-- Deprecated ``modelopt.torch._deploy.utils.get_onnx_bytes`` API. Please use ``modelopt.torch._deploy.utils.get_onnx_bytes_and_metadata`` instead to access the ONNX model bytes with external data. see `examples/onnx_ptq/download_example_onnx.py <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/onnx_ptq/download_example_onnx.py>`_ for example usage.
+- Deprecated ``modelopt.torch._deploy.utils.get_onnx_bytes`` API. Please use ``modelopt.torch._deploy.utils.get_onnx_bytes_and_metadata`` instead to access the ONNX model bytes with external data. see `examples/onnx_ptq/download_example_onnx.py <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/onnx_ptq/download_example_onnx.py>`_ for example usage.
 
 **New Features**
 
@@ -40,7 +40,7 @@ Model Optimizer Changelog (Linux)
 - Add support for ``nemotron-post-training-dataset-v2`` and ``nemotron-post-training-dataset-v1`` in ``examples/llm_ptq``. Default to a mix of ``cnn_dailymail`` and ``nemotron-post-training-dataset-v2`` (gated dataset accessed using ``HF_TOKEN`` environment variable) if no dataset is specified.
 - Allow specifying ``calib_seq`` in ``examples/llm_ptq`` to set the maximum sequence length for calibration.
 - Add support for MCore MoE PTQ/QAT/QAD.
-- Add support for multi-node PTQ and export with FSDP2 in ``examples/llm_ptq/multinode_ptq.py``. See `examples/llm_ptq/README.md <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/llm_ptq#multi-node-post-training-quantization-with-fsdp2>`_ for more details.
+- Add support for multi-node PTQ and export with FSDP2 in ``examples/llm_ptq/multinode_ptq.py``. See `examples/llm_ptq/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/llm_ptq#multi-node-post-training-quantization-with-fsdp2>`_ for more details.
 - Add support for Nemotron Nano VL v1 & v2 models in FP8/NVFP4 PTQ workflow.
 - Add flags ``nodes_to_include`` and ``op_types_to_include`` in AutoCast to force-include nodes in low precision, even if they would otherwise be excluded by other rules.
 - Add support for ``torch.compile`` and benchmarking in ``examples/diffusers/quantization/diffusion_trt.py``.
@@ -51,15 +51,15 @@ Model Optimizer Changelog (Linux)
 
 **Documentation**
 
-- Add general guidelines for Minitron pruning and distillation. See `examples/pruning/README.md <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/pruning#pruning-guidelines>`_ for more details.
-- Added example for exporting QLoRA checkpoint for vLLM deployment. Refer to `examples/llm_qat/README.md <https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/79ef31bc7269ba4da0cfab446da5b64509cbfcef/examples/llm_qat/README.md#qlora-deployment>`_ for more details
+- Add general guidelines for Minitron pruning and distillation. See `examples/pruning/README.md <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/pruning#pruning-guidelines>`_ for more details.
+- Added example for exporting QLoRA checkpoint for vLLM deployment. Refer to `examples/llm_qat/README.md <https://github.com/NVIDIA/Model-Optimizer/blob/79ef31bc7269ba4da0cfab446da5b64509cbfcef/examples/llm_qat/README.md#qlora-deployment>`_ for more details
 
 0.37 (2025-10-08)
 ^^^^^^^^^^^^^^^^^
 
 **Deprecations**
 
-- Deprecated ModelOpt's custom docker images. Please use the PyTorch, TensorRT-LLM or TensorRT docker image directly or refer to the `installation guide <https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html>`_ for more details.
+- Deprecated ModelOpt's custom docker images. Please use the PyTorch, TensorRT-LLM or TensorRT docker image directly or refer to the `installation guide <https://nvidia.github.io/Model-Optimizer/getting_started/2_installation.html>`_ for more details.
 - Deprecated ``quantize_mode`` argument in ``examples/onnx_ptq/evaluate.py`` to support strongly typing. Use ``engine_precision`` instead.
 - Deprecated TRT-LLM's TRT backend in ``examples/llm_ptq`` and ``examples/vlm_ptq``. Tasks ``build`` and ``benchmark`` support are removed and replaced with ``quant``. ``engine_dir`` is replaced with ``checkpoint_dir`` in ``examples/llm_ptq`` and ``examples/vlm_ptq``. For performance evaluation, please use ``trtllm-bench`` directly.
 - ``--export_fmt`` flag in ``examples/llm_ptq`` is removed. By default we export to the unified Hugging Face checkpoint format.
@@ -226,16 +226,16 @@ Model Optimizer Changelog (Linux)
 - Disabled saving modelopt state in unified hf export APIs by default, i.e., added ``save_modelopt_state`` flag in ``export_hf_checkpoint`` API and by default set to False.
 - Add FP8 and NVFP4 real quantization support with LLM QLoRA example.
 - The :class:`modelopt.deploy.llm.LLM` now support use the :class:`tensorrt_llm._torch.LLM` backend for the quantized HuggingFace checkpoints.
-- Add `NVFP4 PTQ example for DeepSeek-R1 <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/deepseek>`_.
-- Add end-to-end `AutoDeploy example for AutoQuant LLM models <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/llm_autodeploy>`_.
+- Add `NVFP4 PTQ example for DeepSeek-R1 <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/deepseek>`_.
+- Add end-to-end `AutoDeploy example for AutoQuant LLM models <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/llm_autodeploy>`_.
 
 0.23 (2025-01-29)
 ^^^^^^^^^^^^^^^^^
 
 **Backward Breaking Changes**
 
 - Support TensorRT-LLM to 0.17. Examples (e.g. benchmark task in llm_ptq) may not be fully compatible with TensorRT-LLM 0.15.
-- Nvidia TensorRT Model Optimizer has changed its LICENSE from NVIDIA Proprietary (library wheel) and MIT (examples) to Apache 2.0 in this first full OSS release.
+- Nvidia Model Optimizer has changed its LICENSE from NVIDIA Proprietary (library wheel) and MIT (examples) to Apache 2.0 in this first full OSS release.
 - Deprecate Python 3.8, Torch 2.0, and Cuda 11.x support.
 - ONNX Runtime dependency upgraded to 1.20 which no longer supports Python 3.9.
 - In the Huggingface examples, the ``trust_remote_code`` is by default set to false and require users to explicitly turning it on with ``--trust_remote_code`` flag.
@@ -283,7 +283,7 @@ Model Optimizer Changelog (Linux)
 **Backward Breaking Changes**
 
 - Deprecated the summarize task in the ``llm_ptq`` example.
-- Deprecated the ``type`` flag in the `huggingface_example.sh <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/llm_ptq/scripts/huggingface_example.sh>`_
+- Deprecated the ``type`` flag in the `huggingface_example.sh <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/llm_ptq/scripts/huggingface_example.sh>`_
 - Deprecated Python plugin support in ONNX.
 - Support TensorRT-LLM 0.13. Examples not compatible with TensorRT-LLM 0.12.
 - :meth:`mtq.auto_quantize <modelopt.torch.quantization.model_quant.auto_quantize>` API has been updated. The API now
@@ -320,7 +320,7 @@ Model Optimizer Changelog (Linux)
 - New APIs and examples: :mod:`modelopt.torch.prune` for pruning Conv, Linear, and Attention heads for
   NVIDIA Megatron-core GPT-style models (e.g. Llama 3), PyTorch Computer Vision models, and HuggingFace Bert/GPT-J models.
 - New API: :mod:`modelopt.torch.distill` for knowledge distillation, along with guides and example.
-- New Example: `HF BERT Prune, Distill & Quantize <https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/examples/chained_optimizations>`_
+- New Example: `HF BERT Prune, Distill & Quantize <https://github.com/NVIDIA/Model-Optimizer/blob/main/examples/chained_optimizations>`_
   showcasing how to chain pruning, distillation, and quantization to achieve the best performance on a given model.
 - Added INT8/FP8 DQ-only support for ONNX model.
 - New API: :mod:`modelopt.torch.speculative` for end-to-end support of Medusa models.
@@ -383,13 +383,13 @@ Model Optimizer Changelog (Linux)
 
 **Backward Breaking Changes**
 
-- `PTQ examples <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/llm_ptq>`_ have been
+- `PTQ examples <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/llm_ptq>`_ have been
   upgraded to use TensorRT-LLM 0.10.
 
 **New Features**
 
 - Adding TensorRT-LLM checkpoint export support for Medusa decoding (official ``MedusaModel`` and Megatron Core ``GPTModel``).
-- Enable support for mixtral, recurrentgemma, starcoder, qwen in `PTQ examples <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/llm_ptq>`_.
+- Enable support for mixtral, recurrentgemma, starcoder, qwen in `PTQ examples <https://github.com/NVIDIA/Model-Optimizer/tree/main/examples/llm_ptq>`_.
 - Adding TensorRT-LLM checkpoint export and engine building support for sparse models.
 - Import scales from TensorRT calibration cache and use them for quantization.
 - (Experimental) Enable low GPU memory FP8 calibration for the Hugging Face models when the original model size does not fit into the GPU memory.
@@ -403,7 +403,7 @@ Model Optimizer Changelog (Linux)
 **Backward Breaking Changes**
 
 - [!!!] The package was renamed from ``ammo`` to ``modelopt``. The new full product
-  name is *Nvidia TensorRT Model Optimizer*. PLEASE CHANGE ALL YOUR REFERENCES FROM ``ammo`` to
+  name is *Nvidia Model Optimizer*. PLEASE CHANGE ALL YOUR REFERENCES FROM ``ammo`` to
   ``modelopt`` including any paths and links!
 - Default installation ``pip install nvidia-modelopt`` will now only install minimal core
   dependencies. Following optional dependencies are available depending on the features that are

CONTRIBUTING.md

@@ -1,17 +1,17 @@
-# Contributing to TensorRT Model Optimizer
+# Contributing to Model Optimizer
 
-Thanks for your interest in contributing to TensorRT Model Optimizer (ModelOpt)!
+Thanks for your interest in contributing to Model Optimizer (ModelOpt)!
 
 ## 🛠️ Setting up your environment
 
-Ensure that TensorRT Model Optimizer (ModelOpt) is installed in editable mode and that all `dev` optional requirements are installed:
+Ensure that Model Optimizer (ModelOpt) is installed in editable mode and that all `dev` optional requirements are installed:
 
 ```bash
 pip install -e ".[dev]"
 ```
 
 If you are working on features that require dependencies like TensorRT-LLM or Megatron-Core, consider using a docker container to simplify the setup process.
-Visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.
+Visit our [installation docs](https://nvidia.github.io/Model-Optimizer/getting_started/2_installation.html) for more information.
 
 ## 🧹 Code linting and formatting