I was actually just looking into fine-tuning an LLM for Magic: The Gathering this week -- I've been building a small card-similarity browser using semantic embeddings of cards to find functionally or flavorfully similar cards.
I've just been using InstructorXL, but either Instructor doesn't have enough innate knowledge of the game, or else I need to work on better prompts, but so far I've tried 9 different prompts, and none of them seem to perform very well for generating embeddings:
So my next step was to try and download a dataset of similar cards (I have some ideas on this), and I was trying to see if I could use this to do triplet-loss training of a large embedding model or something.
Aaaaand, that's as far as I've gotten. I haven't actually figured out _how_ to hook all of that up, but your post is extremely inspirational for me. Thank you for posting this!!
I was actually just looking into fine-tuning an LLM for Magic: The Gathering this week -- I've been building a small card-similarity browser using semantic embeddings of cards to find functionally or flavorfully similar cards.
I've just been using InstructorXL, but either Instructor doesn't have enough innate knowledge of the game, or else I need to work on better prompts, but so far I've tried 9 different prompts, and none of them seem to perform very well for generating embeddings:
https://github.com/HanClinto/MtgMatrix/blob/main/data/create...
So my next step was to try and download a dataset of similar cards (I have some ideas on this), and I was trying to see if I could use this to do triplet-loss training of a large embedding model or something.
Aaaaand, that's as far as I've gotten. I haven't actually figured out _how_ to hook all of that up, but your post is extremely inspirational for me. Thank you for posting this!!