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Comment by andrewljohnson | Hacker News Reader

the shakespeare code tuned a little with different training data does a good job of generating Magic The Gathering commander decks

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Somewhat related: I wrote up a MTG card generator based on nanoGPT a while ago that I think produces pretty good results for being 1m parameters.

The real neat thing about this is that WotC makes a few thousand new cards each year, so my training data set just grows over time and the model gets better with no effort spent on my part.

https://github.com/jlwitthuhn/TCGGPT

It would be interesting to come up with a use case which requires a freshly trained model and isn't just something that generic models can already, especially with 1MM context window

would love more details on this. this is exactly the type of project I'd like to dabble in to get more up to speed.

People have been doing this for a while.

https://x.com/roborosewater

https://bsky.app/profile/roborosewaterm.bsky.social

You can see the invention of RLHF/ChatGPT here because text generation suddenly became much more coherent and also much less interesting. You have to go back to older tech for surrealism because nobody will let you see the good stuff (the base models).

I guess I was much more interested in being able to work with an LLM to create good, synergistic Commander decks and less interested in generating custom Magic cards.

I'm sure I can dig up info on how to do this and piece it together, but I thought OP might have a guide specifically for it.

FWIW, there was a pretty popular post on HN around generating MTG cards using AI a couple years back but I believe that their approach was a fine-tune on an existing LLM.

https://news.ycombinator.com/item?id=37427854

I like the idea of specific-purpose toy models. How did you tune the code and what dataset you used?

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