ao

ve it is a fad that will blow over. The ones in this repo aren't stellar about it, but at 500 lines it's easily achievable to type hint the code yourself, which can serve as an excellent learning opportunity 3. PyTorch offers some fun conversion tools, also, allowing one to compare-and-contrast how it executes under Torch versus ONNX <https://docs.pytorch.org/docs/stable/onnx.html>, TorchScript <https://docs.pytorch.org/docs/stable/generated/torch.jit.sav...>, CoreML <https://apple.github.io/coremltools/docs-guides/source/conve...>, or a bazillion other competing frameworks 4. Related, one can play around with quantization and other "inference related" concerns (e.g. https://github.com/pytorch/ao#pytorch-native-training-to-ser... ) 5. Further related, one can play around with the fine-tuning mentioned elsewhere, to better understand what is and isn't possible to achieve using that process. Because the code is digestable, and the models are reasonably sized (Qwen 0.6B weighs only 1.4GB and is Apache 2), it brings FAFO opportunities in ways that gpt-oss-20b (or bigger!) won't I do appreciate that some of what I said may skate close to "ML engineer" concerns, so obviously your situation will be different, but for me having a better grip on how these things work enables me to have better conversations with my colleagues and also helps trip my bullshit detector when someone claims they're the second coming and are going to cure cancer or whatever

cuda and triton ops. Most of those discussions have been happening on #torchao on discord.gg/gpumode so if you need to chat back and forth feel free to reach out to the team there otherwise Github also works.