Release AMD Quark Release 0.11.1 · amd/Quark

Supported out-of-box model architectures:

Added File-to-File quantization for ultra-large models. This mode supports weight-only quantization and dynamic activation quantization + weight quantization, exports hf_format only, and can also accept pre-quantized inputs (deepseek-style FP8, compressed-tensors) and re-quantize them to a different format.

For example, the command below runs file-to-file quantization to MXFP4:
```
python3 quantize_quark.py --model_dir [model checkpoint folder] \
                          --output_dir [output folder] \
                          --quant_scheme mxfp4 \
                          --file2file_quantization \
                          --skip_evaluation
```
Added a pre-quantization compatibility check for transformers in LLM PTQ workflows, and enabled dry-run compatibility checking by default with clearer error messages when model loading fails.

Fixed weight calibration coverage to ensure complete calibration even for weights outside the forward path, and added token distribution coverage warnings during calibration.

Support using a YAML file as input to perform custom preprocessing for float models before quantization.

Memory optimization has been extended to all calibration methods, particularly further reducing memory usage during activation data collection.

Infer kernel size from weights if the attribute kernel_shape of Conv nodes are not presented explicitly during fast finetuning.
Fix scale extraction to handle int32_data in ONNX initializers.
Add optional config parameter to onnxslim optimization.
Update requirements.txt for onnxslim from 0.1.77 to 0.1.84.
Handle NaN and Inf values in model inference output.

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