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PokeLLMon

This is an experiment to see how much information we can teach a pretrained model (like Phi-3) using LoRA, and then how much we can actually extract using QA.

It's kinda like the Physics of LLMs part 3.1, where they take a model, train it on a bunch of facts, then ask questions about those facts. I want to see if I can do a forward pass for facts (pokemon types) for ALL Pokemon, train on a subset of Pokemon questions about types, and see if the model can generalize to unseen pokemon.

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Fine-tuning LLMs with facts about Pokémon

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