In the tutorials of Quantization, there is a mention that the model size would be reduced by using Dynamic Quantization(DQ).
After I merged the DQ codes, I found the model size is reduces(5MB->2MB). that what I expected.
However, I am wondering why the model is reduces.
so, I tried to log the model state_dict and the log is following.

the original model’s state_dict()
				'model.layers.3.residual_group.blocks.5.mlp.fc1.weight', tensor([[-0.0133, -0.0458, -0.0438,  ..., -0.0109,  0.0203, -0.0292],
				[ 0.0185,  0.0241,  0.0071,  ...,  0.0204,  0.0048, -0.0240],
				[-0.0027, -0.0198, -0.0116,  ..., -0.0246, -0.0079, -0.0145],
				...,
				[-0.0086,  0.0161,  0.0068,  ...,  0.0200,  0.0013, -0.0164],
				[ 0.0080, -0.0006, -0.0074,  ...,  0.0420, -0.0109,  0.0062],
				[-0.0169,  0.0129,  0.0252,  ..., -0.0208, -0.0016, -0.0064]])), ('model.layers.3.residual_group.blocks.5.mlp.fc1.bias', tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
				0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
				0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
				0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
				0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])), 


this is DQ’s state_dict()
				('model.layers.3.residual_group.blocks.5.mlp.fc1.scale', tensor(1.)), 
				
				('model.layers.3.residual_group.blocks.5.mlp.fc1.zero_point', tensor(0)), 
				
				('model.layers.3.residual_group.blocks.5.mlp.fc1._packed_params.dtype', torch.qint8), 
				
				('model.layers.3.residual_group.blocks.5.mlp.fc1._packed_params._packed_params', (tensor([[-0.0131, -0.0459, -0.0435,  ..., -0.0107,  0.0203, -0.0292],
				[ 0.0185,  0.0238,  0.0072,  ...,  0.0203,  0.0048, -0.0238],
				[-0.0024, -0.0197, -0.0119,  ..., -0.0244, -0.0078, -0.0143],
				...,
				[-0.0083,  0.0161,  0.0066,  ...,  0.0197,  0.0012, -0.0161],
				[ 0.0078, -0.0006, -0.0072,  ...,  0.0417, -0.0107,  0.0060],
				[-0.0167,  0.0131,  0.0250,  ..., -0.0209, -0.0018, -0.0066]],
			   size=(120, 60), dtype=torch.qint8,
			   quantization_scheme=torch.per_tensor_affine, scale=0.0005962323630228639,
			   zero_point=0), tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
				0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
				0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
				0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
				0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
			   requires_grad=True))), 

I think DQ’s weight/bias looks like fp32 not int8.
in this reason, I wonder how to reduce model size by using fp32 weight format.
could you tell me why the this is happend?
Thank you for your help.