I’ve been working on the pytorch-generative library in my spare time for a couple of months now as a way to dive deeper into recent work on neural autoregressive generative modeling. The goal of the library is to provide clean, high quality implementations of recent SOTA work as the code for a lot of papers is hard to read and understand. I’m also trying to see what parts can be abstracted and easily reused to make future implementations and experiments easier.
Please take a look! I’m happy to answer any questions and would love any feedback.
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Hi HN!
I’ve been working on the pytorch-generative library in my spare time for a couple of months now as a way to dive deeper into recent work on neural autoregressive generative modeling. The goal of the library is to provide clean, high quality implementations of recent SOTA work as the code for a lot of papers is hard to read and understand. I’m also trying to see what parts can be abstracted and easily reused to make future implementations and experiments easier.
Please take a look! I’m happy to answer any questions and would love any feedback.