Hi. Thank you for the reply. Neither model gives errors in saving or loading, nor inference. The code I used to check the behaviour was the following:
model, _ = generate_model(args)
fp_model = resume_model(
     (args.model_path / 'full_precision' / 'mobilenet3d_ft_50ep.pth'), model)

quant_model = torch.jit.load((args.model_path / 'quantized' / 'quant_mobilenet3d_ft_50ep.pth').as_posix())

for fp_name, _ in fp_model.named_parameters():
    print(fp_name)

for q_name, _ in quant_model.named_parameters():
    print(q_name)

Just loading both models and checking their parameters. This example is from a MobileNet3D and even though the layers are not named I can easily assess their class based on their index.
Extract from full precision model (missing indices 2 and 5, ReLUs, as expected):

features.17.conv.0.weight
features.17.conv.1.weight
features.17.conv.1.bias
features.17.conv.3.weight
features.17.conv.4.weight
features.17.conv.4.bias
features.17.conv.6.weight
features.17.conv.7.weight
features.17.conv.7.bias

Extract from quantized version (missing ReLUs and Convs, not expected):

features.17.conv.1.weight
features.17.conv.1.bias
features.17.conv.4.weight
features.17.conv.4.bias
features.17.conv.7.weight
features.17.conv.7.bias

Interestingly, I also checked with the quantized version of a SqueezeNet3D which was giving abysmal inference results (random basically) and the list of parameters is empty. This architecture has some 3D pooling modules which are not supported, and I had to compute them in floating-point. Still, I cannot understand how can I get no errors nether in loading, saving or inference.