If I try this simple example from the docs
import torch

# define a floating point model
class M(torch.nn.Module):
    def __init__(self):
        super(M, self).__init__()
        self.fc = torch.nn.Linear(4, 4)

    def forward(self, x):
        x = self.fc(x)
        return x

# create a model instance
model_fp32 = M()
# create a quantized model instance
model_int8 = torch.quantization.quantize_dynamic(
    model_fp32,  # the original model
    {torch.nn.Linear},  # a set of layers to dynamically quantize
    dtype=torch.qint8)  # the target dtype for quantized weights

# run the model
input_fp32 = torch.randn(4, 4, 4, 4)
res = model_int8(input_fp32)

I can check the weights of fc layer with
print(model_int8.fc.weight())
And the output will be:
tensor([[-0.0307,  0.1806,  0.3189,  0.0692],
        [ 0.1306, -0.1114,  0.1306, -0.4227],
        [-0.1575, -0.1729, -0.4841, -0.2997],
        [ 0.2036,  0.0307,  0.4880,  0.3804]], size=(4, 4), dtype=torch.qint8,
       quantization_scheme=torch.per_tensor_affine, scale=0.0038423435762524605,
       zero_point=0)

It says that weights are of qint8 type