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light24bulbs t1_jdrm9kh wrote

Reply to comment by baffo32 in [R] Hello Dolly: Democratizing the magic of ChatGPT with open models by austintackaberry

That's the part I wasn't getting. I assumed the fine tuning involved a different process. I see now that it is fact just more training data, often templated into a document in such a way that it's framed clearly for the LLM.

The confusing thing is that most of the LLM-as-a-service companies, Open-AI included, will ONLY take data in the question answer format, as if that's the only data you'd want to use to fine tune.

What if i want to feed a book in so we can talk about the book? A set of legal documents? Documentation of my project? Transcriptions of TV shows?

There are so many use cases for training on top of an already pre-trained LLM that aren't just question answering.

I'm into training llama now. I simply took some training code i found, removed the JSON parsing question answer templating stuff, and done.

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nemorocksharder t1_jdz8kt5 wrote

What you're describing is exactly what I have been looking to do too, and am really surprised I'm not hearing more about it. Have you found any useful approaches to essentially adding to the LLM's Corpus with target material/text? or anyone else trying to do this?

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