loss.backward()
        optimizer.step()
```
Apart from the code, we are also releasing a tutorial for fine-tuning multimodal foundation models, and a blog post (with code pointers) on how to scale up such models using techniques from PyTorch Distributed (FSDP and activation checkpointing). We hope such examples and tutorials will serve to demystify a number of advanced features available in the PyTorch ecosystem.
What’s Next?
While this is an exciting launch, there’s a lot more to come. The library is under development and we are working on adding some of the exciting developments in the space of diffusion models, and examples to showcase common trends from research. As you explore and use the library, we’d love to hear any feedback you might have! You can find more details on how to get started here.
Team