Comments (2)
For Conv2D questions, i don't think there's need to insert additional dequantize/quantize before next QuantizedConv2DWithBiasAndRelu as this op supports s8 input.
For LTSM, i remember we already supported LTSM mode since v1.6 release
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close it if no further questions
from neural-compressor.
Related Issues (20)
- Per tensor quantization in smoothquant HOT 2
- RTN sym behavior is not aligned HOT 1
- AutoRound sym is not aligned
- AWQ Quantization padding error HOT 1
- io.UnsupportedOperation: fileno HOT 1
- 'q_config' is needed when export an INT8 model HOT 3
- How to load quantized LLM and do inference? HOT 5
- Continue quantization from history.snapshot HOT 4
- how to extract int8 weights from quantized model HOT 8
- I tried to fine-tune and prune the Helsinki-opus-MT series model, but an error occurred. HOT 3
- Is there any accuracy data related to FP4?
- PTQ with IPEX backend and XPU device is not working HOT 3
- FP4 encoding related
- Error in fp8 quantization: Invalid scale factor : 1.70e+06, make sure the scale is not larger than : 6.55e+04
- Any example to quantise a text embedding model on Intel Gaudi2? HOT 2
- Dataset Selection for Post-Training Quantization (PTQ) HOT 5
- Why is LayerNorm not quantized to int8 in PTQ? HOT 3
- Model Size Increase After PTQ HOT 8
- Quantization failed HOT 1
- how to evaluate AWQ ? HOT 6
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