Comments (2)
May be partially addressed in #302
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#302 merged, the situation has improved for the better - precision init can now select different precisions for branches. Requantization is, however, not considered both in AutoQ and HAWQ w.r.t. performance impact.
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Related Issues (20)
- Compressed models that call torch.is_floating_point() during inference are traced with runtime error.
- nncf + ultralytics yolov8 training-time compression HOT 7
- Ultralytics yolov8 QAT example HOT 1
- [Good First Issue] [NNCF] Make NNCF common utils code pass mypy checks HOT 23
- [Good First Issue] [NNCF] Make NNCF common accuracy aware training code pass mypy checks HOT 17
- [Good First Issue] [NNCF] Make NNCF common tensor statistics code pass mypy checks HOT 10
- Thanks to our Contributors HOT 1
- [Good First Issue][NNCF]: Add INT8 weight compression conformance test for Tinyllama-1.1b PyTorch model HOT 19
- [Good First Issue][NNCF]: Fixing NNCFGraph export for visualization in Netron HOT 6
- Why doesn't the size and precision of the model change after INT4 quantization? HOT 2
- [Good First Issue][NNCF]: Optimize memory footprint by removing redundant collected statistics HOT 8
- [Good First Issue][NNCF]: Dump actual_subset_size to ov.Model HOT 13
- [Good First Issue][NNCF]: dump the ignored scope more gracefully HOT 4
- [Good First Issue][NNCF]: check number of u8, u4 constants in weight compression tests HOT 10
- PTQ of Fast R-CNN crashes in PyTorch backend HOT 1
- [Good First Issue][NNCF]: fix invalid error reporting in JSON schema HOT 19
- [Good First Issue][NNCF]: Add tests for torch device utils HOT 5
- [Good First Issue][NNCF]: Remove compress_to_fp16=False from examples HOT 3
- AttributeError: 'list' object has no attribute 'keys' when executing yolov8_quantize_with_accuracy_control example HOT 4
- The question about function create_compressed_model():RuntimeError: CUDA error: device-side assert triggered HOT 4
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