### What does this PR do?
Type of change: new feature
Adds opt-in support for using the standalone TensorRT-RTX ABI Execution
Provider during ModelOpt ONNX quantization.
Users select the ABI backend with:
`--calibration_eps=NvTensorRtRtx --trt_rtx_backend=abi`
When selected, ModelOpt imports and registers the installed TensorRT-RTX
ABI provider before creating the ONNX Runtime inference session. The
backend selection is propagated through INT8, FP8, and INT4 AWQ
calibration paths, including the Windows GenAI LLM quantization example.
The existing `--calibration_eps=NvTensorRtRtx` behavior remains backward
compatible. The `legacy` backend is still the default and continues to
use TensorRT-RTX libraries supplied through `PATH`.
For Windows x64 with Python 3.11 or newer, the ONNX dependencies now
include:
- `onnxruntime-gpu~=1.26.0`
- `onnxruntime-ep-nv-tensorrt-rtx-cu13==0.4.0`
Keeping `onnxruntime-gpu` allows users to select either CUDA EP or
TensorRT-RTX ABI EP for calibration. Windows-on-Arm source-build
instructions are intentionally out of scope and will be documented
separately.
### Usage
```powershell
python -m modelopt.onnx.quantization `
--onnx_path="C:\path\to\Llama-3.2-3B-Instruct\model.onnx" `
--model_id="C:\path\to\Llama-3.2-3B-Instruct\config.json" `
--quantize_mode=int8 `
--output_path="C:\path\to\int8_abi\model.onnx" `
--calibration_eps=NvTensorRtRtx `
--trt_rtx_backend=abi `
--use_external_data_format `
--high_precision_dtype=fp32 `
--log_level=INFO
### Testing
unit test have been added
### 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`: ✅
- Did you write any new necessary tests?: ✅
- Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅
- Did you get Claude approval on this PR?: pending
<!-- This is an auto-generated comment: release notes by coderabbit.ai -->
## Summary by CodeRabbit
- **New Features**
- Added optional TensorRT-RTX ABI backend support for ONNX calibration on Windows ARM64.
- Added `legacy` and `abi` backend selection to quantization APIs and command-line tools; `legacy` remains the default.
- Added validation for unsupported backends and incompatible TensorRT plugin configurations.
- Updated Windows ARM64 installation support and platform-specific package configuration.
- **Documentation**
- Updated Windows installation guidance, Python compatibility requirements, ARM64 setup, and verification steps.
- Documented the new TensorRT-RTX backend command-line option.
- **Tests**
- Added coverage for ABI provider registration, backend validation, and compatibility checks.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Haoxi Zhang <haoxiz@nvidia.com>
### What does this PR do?
Add WoA env setup guide. Includes build instruction of pyarrow, which
used by datatsets
### Usage
N/A
### Testing
N/A
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: N/A
- 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?: N/A
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A
- Did you get Claude approval on this PR?: N/A
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Documentation**
- Added comprehensive Windows on Arm installation guidance, including
prerequisites, environment setup, dependency installation, verification,
and troubleshooting.
- Documented experimental Windows ARM64 support, supported quantization
formats, native dependency requirements, and Support Matrix details.
- Expanded supported Windows Python versions through 3.13.
- Expanded TensorRT-RTX guidance for calibration, deployment, provider
setup, and standalone plugin usage.
- Clarified PyArrow requirements and local build instructions for
Windows ARM64.
- Added links to dedicated Windows on Arm installation resources and
shared TensorRT-RTX documentation.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Haoxi Zhang <haoxiz@nvidia.com>
### What does this PR do?
Type of change: Documentation update
- Update documentation guide for ONNX INT4 PTQ on Windows cuda13 host -
mention about compatible onnxruntim-gpu and cupy-cuda13x packages.
### Testing
- Windows's onnx_ptq\genai_llm INT4 PTQ example with a 1B genai-cuda-ep
ONNX model + local doc building
### 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
* **Documentation**
* Clarified Windows CUDA prerequisites for calibration and
GPU-accelerated quantization.
* Added setup guidance for CUDA 12 and CUDA 13.x, including compatible
packages and cuDNN requirements.
* Expanded installation verification steps for CUDA, ONNX Runtime, and
CuPy.
* Updated the GenAI LLM example with CUDA version compatibility
guidance.
* **Enhancements**
* Added runtime logging of detected CUDA environment paths and version
details during quantization.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: vipandya <vipandya@nvidia.com>
Ubuntu 26.04 is here and very soon, NVIDIA PyTorch containers will ship
with Python 3.14 requiring us to enable untested support to unblock them
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* DFlash offline speculative decoding training
* MXFP4→NVFP4 weight conversion support
* Shared hidden-state dump utilities
* Updated DeepSeek PTQ calibration defaults
* **Chores**
* Added Python 3.14 support; updated Python requirement to <3.15
* **Documentation**
* Updated installation documentation for Python version compatibility
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
### What does this PR do?
Type of change: ? documentation
<!-- Details about the change. -->
Update windows documentation for onnxruntime quantization with Cuda13.x
### 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. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Updated Windows installation guide with CUDA 13.x-specific setup
instructions for GPU-accelerated dependencies, including CuPy and ONNX
Runtime configuration with nightly builds.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Signed-off-by: ynankani <ynankani@nvidia.com>
## Summary
- Replaces the old pip license notice ("Please review the license terms
of ModelOpt and any dependencies before use") with the Legal-approved
wording: "Model Optimizer will download and install additional
third-party open source software projects. Review the license terms of
these open source projects before use."
- Adds a generic container license review notice ("Before pulling and
using the container images, please review their respective license
terms.") to the Linux installation doc (Docker tab) and README.
- Adds a `.. note::` with the pip notice to the Windows installation
page (covers both standalone and Olive child pages).
- Expands the README container section to explicitly list all four
recommended NVIDIA container images (`pytorch`, `nemo`, `tensorrt-llm`,
`tensorrt`).
## Test plan
- [x] Verify rendered docs look correct (`nox -s docs`)
- [x] Confirm legal notices appear in Linux, Windows, and README install
sections
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Updated installation guides with explicit references to supported
NVIDIA container images (PyTorch, NeMo, TensorRT-LLM and variants),
clarified pre-installed Model Optimizer in some images, and added notes
to review each container’s license terms; clarified conditional
environment setup wording and local install license guidance.
* **Chores**
* Updated project license header year.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
### What does this PR do?
- Add experimental support for transformers >=5.0 and remove deprecated
usages:
https://github.com/huggingface/transformers/blob/main/MIGRATION_GUIDE_V5.md
- ⚠️ For accelerate examples that used `--warmup-ratio: float`
(deprecated in 5.x), we now change it to `--warmup-steps: float | int`
which works as ratio if float but only for 5.x. For 4.x, it will error
out if float and prompt user to change back to `--warmup-ratio` or pass
an int absolute step count.
- ⚠️ Unified Hugging Face checkpoint export for quantized checkpoints
may not work for some models with transformers>=5.0 yet as it requires a
lot of fixes (e.g. change in how MoE experts are organized)
- ~Add Workaround for TRT-LLM's import of deprecated transformers
functions so trt-llm based gpu unit tests work fine. Still deployment
for models needs proper fixes directly in TRT-LLM hence llm/vlm ptq
example tests still run with transformers 4.57~
- Everything except PTQ and Export (mainly MoE) should work fine with
transformers>=5.0
- Bump min torch to 2.8 and enable 2.11 cicd testing
- NOTE: Upcoming Nemo:26.04 container comes with transformers 5.3
### Testing
<!-- Mention how have you tested your change if applicable. -->
- [x] CI/CD tests passing
- [x] Manually tested unit tests, gpu tests with transformers 4.56 and
5.4
- [x] Manually tested example tests (except trt-llm container tests)
with transformers 4.56 and 5.4
- [x] 2-gpu nightly CICD tests manually triggered and passing: [gpu
tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/23867257540),
[example
tests](https://github.com/NVIDIA/Model-Optimizer/actions/runs/23867260643)
### 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`, using
`torch.load(..., weights_only=True)`, avoiding `pickle`, etc.).
- Is this change backward compatible?: ✅ <!--- If ❌, explain why. -->
- If you copied code from any other source, did you follow IP policy in
[CONTRIBUTING.md](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md#-copying-code-from-other-sources)?:
N/A <!--- Mandatory -->
- Did you write any new necessary tests?: ✅ <!--- Mandatory for new
features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ <!--- Only for new features, API changes, critical bug fixes or
backward incompatible changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Make remote-code usage opt-in via a configurable --trust_remote_code
flag across examples and tools.
* **Bug Fixes**
* Improve checkpoint/resume detection and related training guidance to
avoid erroneous errors.
* **Refactor**
* Consolidate dtype/config naming, switch warmup settings from ratio →
steps, and unify tokenizer invocation patterns.
* **Documentation**
* Simplify changelog title and add misc notes for release 0.44.
* **Chores**
* Remove scheduled PR-branch cleanup workflow and relax/remove several
transformers version pins.
* **Tests**
* Adjust test gates, skips, and structures to align with updated deps
and behaviors.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
### What does this PR do?
Type of change: Minor documentation update
- Update documentation (olive installation instructions) to mention
install commands for CUDA EP packages for ORT / ORT-genai.
### Testing
- Locally checked the readme, doc.
### 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. -->
### Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Documentation**
* Updated Windows Olive installation guidance to recommend CUDA-based
ONNX Runtime packages instead of DirectML and added a link to ONNX
Runtime’s Execution-Provider docs for alternative EPs and requirements.
* Simplified Windows examples by removing explicit package install
commands and pointing users to the consolidated Olive setup
instructions.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: vipandya <vipandya@nvidia.com>
### What does this PR do?
- Remove `examples/nemo_run` and other deprecated Nemo 2.0 references
- Add Megatron-Bridge example links where missing
<!-- Details about the change. -->
### 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?: ✅ <!--- 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)?:
✅ <!--- Only for new features, API changes, critical bug fixes or
backward incompatible changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Deprecations**
* Removed deprecated NeMo 2.0 support and related example flows and
utilities.
* **Documentation**
* Updated docs and examples to emphasize Megatron-Bridge / Megatron-LM
and refreshed technique/deployment guidance and links.
* **New Features**
* Added CLI options for additional parallelism (context/expert
tensor/expert model) in Megatron-Bridge distillation.
* **Chores**
* Removed legacy CI configs and refreshed container image tags across
examples and docs.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Fixes https://github.com/NVIDIA/Model-Optimizer/issues/217
## Summary
- Bump `requires-python` from `>=3.10,<3.13` to `>=3.10,<3.14` to
formally include Python 3.13
- Add explicit Python 3.10–3.13 PyPI classifiers for better
discoverability
- Add `py313` to tox CPU unit test and partial-install environment
matrices
- Add Python 3.10–3.13 to the `multi-py` CI matrix in `unit_tests.yml`
## Background
Python 3.13 was previously excluded by the `<3.13` upper bound. Testing
in a related repo with `--ignore-requires-python` confirmed that the
library installs and runs correctly under Python 3.13. This PR lifts the
restriction and wires up CI to verify it going forward.
## Test plan
- [ ] CI `multi-py` job passes on `py313-torch210-tf_latest-unit`
- [ ] `tox -e py313-torch210-tf_latest-unit` passes locally (requires
Python 3.13 installed)
- [ ] `tox -e py313-partial-unit-torch` passes locally
- [ ] No regressions on existing Python 3.10/3.11/3.12 matrix jobs
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Chores**
* Extended Python support: minimum remains 3.10; added official support
up through 3.13 (upper bound advanced accordingly).
* **Tests**
* CI and test matrix expanded to include experimental Python 3.13
coverage.
* **Documentation**
* Installation docs and changelog updated to reflect Python 3.13
support.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Ivan Basov <ibasov@nvidia.com>
Signed-off-by: Ivan Basov <5455484+ivanbasov@users.noreply.github.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:** Bug fix
**Overview:** Upgrade ORT to 1.24.x to fix various bugs (5615343,
5597780, 5371126).
TODO: Verify no regressions by bumping ORT once @ajrasane is back
## Usage
See each bug.
## Testing
See each bug.
## 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/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Bug Fixes
* Upgraded ONNX Runtime to version 1.24.2, addressing multiple reported
issues
* Updated system requirements and installation documentation to reflect
the new ONNX Runtime version
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com>
Signed-off-by: Gwena Cunha <4861122+gcunhase@users.noreply.github.com>
Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
- Upgrade CICD test containers to latest
- Enable torch 2.10 testing in CICD
## Testing
<!-- Mention how have you tested your change if applicable. -->
CI/CD in this PR should pass
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added support for mixed-precision gradient handling with FSDP2.
* **Documentation**
* Updated Linux installation guide with CUDA 13.x support and cupy
dependency guidance.
* **Chores**
* Updated CI/CD workflows and test infrastructure to support PyTorch
2.10 and CUDA 13.
* Updated container image versions and test environment configurations.
* Updated TensorRT-LLM version requirements.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
**Type of change:** documentation <!-- Use one of the following: Bug
fix, new feature, new example, new tests, documentation. -->
**Overview:**
1. Added multi‑backbone support for quantization: --backbone now accepts
space- or comma-separated lists and resolves to a list of backbone
modules.
2. Introduced PipelineManager.iter_backbones() to iterate named backbone
modules and updated get_backbone() to return a single module or a
ModuleList for multi‑backbone.
3. Updated ExportManager to save/restore per‑backbone checkpoints when a
directory is provided, with {backbone_name}.pt files, and to create
target directories when missing.
4. Simplified save_checkpoint() calls to rely on the registered
pipeline_manager by default.
**Usage: **
```bash
python quantize.py --model wan2.2-t2v-14b --format fp4 --batch-size 1 --calib-size 32 \
--n-steps 30 --backbone transformer transformer_2 --model-dtype BFloat16 \
--quantized-torch-ckpt-save-path ./wan22_mo_ckpts \
--hf-ckpt-dir ./wan2.2-t2v-14b
```
Plans
- [x] [1/4] Add the basic functionalities to support limited image
models with NVFP4 + FP8, with some refactoring on the previous LLM code
and the diffusers example. PIC: @jingyu-ml
- [x] [2/4] Add support to more video gen models. PIC: @jingyu-ml
- [x] [3/4] Add test cases, refactor on the doc, and all related README.
PIC: @jingyu-ml
- [ ] [4/4] Add the final support to ComfyUI. PIC @jingyu-ml
## Testing
<!-- Mention how have you tested your change if applicable. -->
## 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/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: No <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**:No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Release Notes
* **New Features**
* Unified Hugging Face export support for diffusers pipelines and
components
* LTX-2 and Wan2.2 (T2V) support in diffusers quantization workflow
* Comprehensive ONNX export and TensorRT engine build documentation for
diffusion models
* **Documentation**
* Updated to clarify support for both transformers and diffusers models
in unified export API
* Expanded diffusers examples with LoRA fusion guidance and additional
model options (Flux, SD3, SDXL variants)
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
## What does this PR do?
Documentation
**Overview:**
- Update support matrix, changelog, deployment page, example readmes as
per recent feature and model support on Windows side.
## Testing
- No testing, its just documentation change
## 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/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)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->
## Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added ONNX Mixed Precision Weight-only quantization (INT4/INT8)
support.
* Introduced diffusion-model quantization on Windows.
* Added new accuracy benchmarks (Perplexity and KL-Divergence).
* Expanded deployment with multiple ONNX Runtime Execution Providers
(CUDA, DirectML, TensorRT-RTX).
* **Bug Fixes**
* Fixed ONNX 1.19 compatibility issue with CuPy during INT4 AWQ
quantization.
* **Documentation**
* Updated installation guides with system requirements and multiple
backend options.
* Reorganized deployment documentation with comprehensive execution
provider guidance.
* Expanded example workflows with improved setup instructions and
support matrices.
<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: vipandya <vipandya@nvidia.com>
- [x] Product Rename: TensorRT Model Optimizer to Model Optimizer
(OMNIML-3033)
- [x] Mention in Latest News section with date on the date of merging
this PR (12/08)
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
## What does this PR do?
Bump TRT-LLM docker from 1.1.0rc2.post2 (CUDA 12) to 1.2.0rc4 (CUDA 13)
## Testing
<!-- Mention how have you tested your change if applicable. -->
- [ ] 2-gpu tests for all examples: ?
## 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.
- **Is this change backward compatible?**: Yes <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: Yes
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
haoxiz-nvidia