Hello! 
I was thinking that it could be useful to save metadata about a model when you export it via torchscript. Example of such metadata could e.g. be the git-hash that the model was trained under.
Some example code
import torch
import torch.nn as nn

class Mymodel(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(10, 10, 1, 1, 0, bias=True)
        self.accuracy = 0

    @torch.jit.export
    def model_info(self):
        return {
            "git_hash": "*git-hash-info*",
            "created": "*timestamp*",
            "accuracy": f"{self.accuracy}",
            "pytorch_version": "*version*",
        }

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


model = Mymodel()
model.accuracy = 10
scripted_model = torch.jit.script(model)

model_path = 'testmodel.pt'
scripted_model.save(model_path)

loaded_model = torch.jit.load(model_path)
print(loaded_model)
print(loaded_model.model_info())

I think the practice could really help track your models in many systems. I haven’t seen the technique used before and would love some arguments against it or suggestion improvements.
What kind of information would be useful to embed in a torchscript model apart from what I’ve written above?