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## š Bug
The parametrization API applied to the weights of an RNN circumvents updates to the underlying flattened parameter buffers and hence they do not get updated.
This causes the effect on tā¦
samgd updated
2 years ago
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Hi, I am not able to match the pre-trained weights for the above model file from the output path. Could you guide me on how i can use the v3 model with its pre-trained weights. Thanks
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### Search before asking
- [X] I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar feature requests.
### Description
INT8 model on nvidia gpā¦
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### š” Your Question
I was trying to implement QA fine tuning on my dataset using the collab notebook [Quantization Aware Training YoloNAS on Custom Dataset](https://colab.research.google.com/driveā¦
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## Summary of Contributions (9th Feb)
1) **Improve the number of models in TorchBench that work with Dynamo as a tracer:** These passing rates are now comparable to those from torch.compile using Iā¦
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## Description
## Environment
**TensorRT Version**: 8.5
**NVIDIA GPU**: Jetson Orin Nano developer kit 8gb
**NVIDIA Driver Version**:
**CUDA Version**:11.4
**CUDNN Versionā¦
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## Quick summary
FINN v0.9 and v0.10 work with W1A1 but for W4A4 I encounter an error. Which line should I change depending on binary or non-binary quantization?
## Details
I use ONNX file as inā¦
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This'd be another iframe tab (or two) like Hidden / Alternations; with nutrimatic and/or qat queries. Transadditions are easy, just add another `.` to the query (inside `` for nutrimatic or after `/`ā¦
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## Description
Hello, I am performing int8 quantization on a BERT-like embedding model. I noticed that after quantization, the inference speed is much more slower than FP16, and the output of the tā¦
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63 Dynamo test are failing with "'QuantizationConfig' object has no attribute '__bool__'".
## Repro
`PYTORCH_TEST_WITH_DYNAMO=1 pytest test_quantization.py -v -k test_quantize_pt2e_preserve_handā¦