Proposal to add a beginner-friendly introduction tutorial for TorchAO #3274
Description
Hello TorchAO community,
I would like to contribute a beginner-friendly notebook tutorial that introduces TorchAO to users who are new to model optimization and to TorchAO (or even PyTorch in general).
As someone coming from a different background with limited experience in quantization and model optimization, I found that it can be challenging to understand:
- What TorchAO is,
- What its main capabilities are, and
- How someone can start using it effectively in a simple workflow.
While TorchAO already provides strong documentation and tutorials for quantization, some of them seem to assume a level of prior familiarity that newcomers might not yet have or they may target more advanced workflows. I would like to put together a simple notebook tutorial that demonstrates one simple TorchAO quantization flow on a very small model/toy model (e.g. 2-layer MLP or simple CNN). The goal isn't to duplicate the Quick Start or advanced tutorials, but to provide a high-level guide that can help absolute beginners understand what TorchAO is and when to use it.
The notebook would include clear descriptions and references to relevant PyTorch blog posts and documentation pages that already exist, so that users can easily explore more advanced material as well.
Would this be useful to the community to add under tutorials/ or examples/? I’m also open to suggestions on which specific tutorial topics might be most helpful for newcomers who are just starting out with TorchAO.
I appreciate your consideration and feedback!