Comments (1)
Thanks @kadirnar , I answered in the thread. I think it should work just fine, since it has transformer blocks.
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Related Issues (20)
- Weight Sharding HOT 2
- Support Gemma quantization HOT 2
- Bug of the saved model when applying zero and scale quantization HOT 1
- Expected in.dtype() == at::kInt to be true, but got false HOT 14
- Easy way to run lm evaluation harness HOT 1
- Warning: failed to import the BitBlas backend HOT 7
- TypeError: Object of type dtype is not JSON serializable HOT 11
- RuntimeError: Expected in.dtype() == at::kInt to be true, but got false. HOT 9
- zero and scale quant HOT 1
- `hqq/backends/torchao.py` line 177, KeyError: 'scale' HOT 13
- Quesiton on the speed for generating the response HOT 18
- Question about Quantization HOT 4
- Issue when loading the quantized model HOT 5
- 2-bit quantization representation HOT 3
- module 'torch.library' has no attribute 'custom_op' HOT 4
- bitblas introduces dependency on CUDA version HOT 3
- OSError: libnvrtc.so.12: cannot open shared object file: No such file or directory HOT 1
- About the implentation of .cpu() HOT 1
- 3-bit quantization weight data type issue HOT 10
- RuntimeError: Expected in.dtype() == at::kInt to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.) HOT 1
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