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When converting a model that used int8 quantization aware training, conversion of transposed convolutions followed by a scalar multiplication fails.
The converter isn't able to correctly constant f…
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Hello,
I have tried to make my model comaptible with QAT, according to your guideline.
I started with defining a QuantizeConfig class:
```
LastValueQuantizer = tfmot.quantization.keras.quantizers…
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# Prerequisites
Please answer the following questions for yourself before submitting an issue.
- [ ] I am using the latest TensorFlow Model Garden release and TensorFlow 2.
- [x] I am reporting…
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SO after training the model and pushing the adaptors to HF
```
model.push_to_hub("your_name/lora_model", token = "...") # Online saving
tokenizer.push_to_hub("your_name/lora_model", token = "...") …
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Excellent work!
Can use the CPU in the inference state?
And how much faster than baseline?
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Currently, both trained_model and dataset are available only to people with access to bcv002. Is there a way to get these as I don't have access to this server. Could you please how to get the require…
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Hi,
This is more of a question than a feature request which I don't know where else to post.
So I'm trying to perform quantization aware training to a model that's not of tf.keras.Model type but o…
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Prior to filing: check that this should be a bug instead of a feature request. Everything supported, including the compatible versions of TensorFlow, is listed in the overview page of each technique. …
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### 🐛 Describe the bug
When finished quantized aware training, I got a quantized model after torch.quantization.convert(). But when I convert pytorch model, I happened to meet the problem:
Expecte…
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**Describe the bug**
Hello, I want to do full 8-bit quantization(input, weight all 8-bit) to the network with a bilinear upsampling layer. The fake QAT result in validation set is closed to FP32 re…