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@@ -5,16 +5,17 @@ Model Optimizer Changelog (Linux)
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^^^^^^^^^^^^^^^^^
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**Deprecations**
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- Deprecated ``quantize_mode`` argument in ``examples/onnx_ptq/evaluate.py`` to support strongly typing. Use ``engine_precision`` instead.
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- TRT-LLM's TRT backend in ``examples/llm_ptq`` and ``examples/vlm_ptq``. Tasks ``build`` and ``benchmark`` support are removed and replaced with ``quant``. For performance evaluation, please use ``trtllm-bench`` directly.
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- 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``. For performance evaluation, please use ``trtllm-bench`` directly.
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- ``--export_fmt`` flag in ``examples/llm_ptq`` is removed. By default we export to the unified Hugging Face checkpoint format.
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- ``int8_sq`` quantization format is deprecated from the ``examples/vlm_ptq`` respect to the TensorRT-LLM's torch backend switch. Please refer to the previous releases if this quantization format is needed.
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- ``examples/vlm_eval`` as it depends on the deprecated TRT-LLM's TRT backend.
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**Bug Fixes**
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- ``int8_sq`` quantization format is deprecated from the ``examples/vlm_ptq`` with respect to the TensorRT-LLM's torch backend switch. Please refer to the previous releases if this quantization format is needed.
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- Deprecated ``examples/vlm_eval`` as it depends on the deprecated TRT-LLM's TRT backend.
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**New Features**
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- ``high_precision_dtype`` default to fp16 in ONNX quantization, i.e. quantized output model weights are now FP16 by default.
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- Upgrade TensorRT-LLM dependency to 1.1.0rc2.
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0.35 (2025-09-04)
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^^^^^^^^^^^^^^^^^
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**Bug Fixes**
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- Fix attention head ranking logic for pruning Megatron Core GPT models.
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