0.43.0rc1

Install the 0.43.0rc1 pre-release version using

pip install nvidia-modelopt==0.43.0rc1 --extra-index-url https://pypi.nvidia.com

0.43.0rc0

Install the 0.43.0rc0 pre-release version using

pip install nvidia-modelopt[all]==0.43.0rc0 --extra-index-url https://pypi.nvidia.com

ModelOpt 0.42.0 Release

Bug Fixes

• Fix calibration data generation with multiple samples in the ONNX workflow.

New Features

• Added a standalone type inference option (--use_standalone_type_inference) to ONNX AutoCast as an experimental alternative to ONNX's infer_shapes. This option performs type-only inference without shape inference, which can help when shape inference fails or when you want to avoid extra shape inference overhead.
• Added quantization support for the Kimi K2 Thinking model from the original int4 checkpoint.
• Introduced support for params constraint-based automatic neural architecture search in Minitron pruning (mcore_minitron) as an alternative to manual pruning with export_config. See examples/pruning/README.md for more details.
• Example added for Minitron pruning using the Megatron-Bridge framework, including advanced pruning usage with params-constraint-based pruning and a new distillation example. See examples/megatron_bridge/README.md.
• Supported calibration data with multiple samples in .npz format in the ONNX Autocast workflow.
• Added the --opset option to the ONNX quantization CLI to specify the target opset version for the quantized model.
• Enabled support for context parallelism in Eagle speculative decoding for both HuggingFace and Megatron Core models.
• Added unified Hugging Face export support for diffusers pipelines/components.
• Added support for LTX-2 and Wan2.2 (T2V) in the diffusers quantization workflow.
• Provided PTQ support for GLM-4.7, including loading MTP layer weights from a separate mtp.safetensors file and supporting export as-is.
• Added support for image-text data calibration in PTQ for Nemotron VL models.
• Enabled advanced weight scale search for NVFP4 quantization and its export pathway.
• Provided PTQ support for Nemotron Parse.
• Added distillation support for LTX-2. See examples/diffusers/distillation/README.md for more details.

0.42.0rc2

Install the 0.42.0rc2 pre-release version using

pip install nvidia-modelopt[all]==0.42.0rc2 --extra-index-url https://pypi.nvidia.com

0.42.0rc1

Install the 0.42.0rc1 pre-release version using

pip install nvidia-modelopt==0.42.0rc1 --extra-index-url https://pypi.nvidia.com

0.42.0rc0

Install the 0.42.0rc0 pre-release version using

pip install nvidia-modelopt==0.42.0rc0 --extra-index-url https://pypi.nvidia.com

ModelOpt 0.41.0 Release

Bug Fixes

• Fix Megatron KV Cache quantization checkpoint restore for QAT/QAD (device placement, amax sync across DP/TP, flash_decode compatibility).

New Features

• Add support for Transformer Engine quantization for Megatron Core models.
• Add support for Qwen3-Next model quantization.
• Add support for dynamically linked TensorRT plugins in the ONNX quantization workflow.
• Add support for KV Cache Quantization for vLLM FakeQuant PTQ script. See examples/vllm_serve/README.md for more details.
• Add support for subgraphs in ONNX autocast.
• Add support for parallel draft heads in Eagle speculative decoding.
• Add support to enable custom emulated quantization backend. See register_quant_backend for more details. See an example in tests/unit/torch/quantization/test_custom_backend.py.
• Add examples/llm_qad for QAD training with Megatron-LM.

Deprecations

• Deprecate num_query_groups parameter in Minitron pruning (mcore_minitron). You can use ModelOpt 0.40.0 or earlier instead if you need to prune it.

Backward Breaking Changes

• Remove torchprofile as a default dependency from ModelOpt as it's used only for flops-based FastNAS pruning (computer vision models). It can be installed separately if needed.

0.41.0rc3

0.41.0rc3

0.41.0rc2

0.41.0rc2

0.41.0rc1

0.41.0rc1