Comments (1)
Thanks! It should be similar to .cuda()
but instead would use .to('cpu')
:
Lines 472 to 535 in b1a7c06
RIght now it is a mess because we support quantizing the scale/zero values and support offloading them to the cpu.
I think in the future we are gonna remove this which should make things much easier: #93 (comment)
May I ask why would need the .cpu()
call? If you just want to use HQQLinear
with cpu, you can just pass HQQLinear(...device='cpu')
from hqq.
Related Issues (20)
- Question about quantization. HOT 2
- Is HQQLinearLoRAWithFakeQuant differentiable? HOT 1
- hqq+ lora ValueError || ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' HOT 3
- Activation quantization HOT 9
- Group_Size setting HOT 1
- 1 bit inference HOT 4
- Weird problem in loading quantized_model + lora_adpter
- 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
- 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
- Weight Sharding HOT 1
- 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
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