NVIDIA/Model-Optimizer 0.42.0

ModelOpt 0.42.0 Release

on GitHub

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.