Hi, I’m not too informed about LLMs so I’ll appreciate any correction to what I might be getting wrong. I have a collection of books I would like to train an LLM on so I could use it as a quick source of information on the topics covered by the books. Is this feasible?
Would you recommend fine-tuning over RAG to improve domain specific performance, my end goal would be a small, efficient and very specialised LLM to help get info on the contents of the books (all of them are about the same topic, from different povs and authors)
I would receommend you read over the work of the person who finetuned a mistral model on many us army field guides to understand what fine tuning on a lot of books to bake in knowledge looks like.
If you are a newbie just learning how this technology works I would suggest trying to get RAG working with a small model and one or two books converted to a big text file just to see how it works. Because its cheap/free t9 just do some tool calling and fill up a models context.
Once you have a little more experience and if you are financially well off to the point 1-2 thousand dollars to train a model is who-cares whatever play money to you then go for finetuning.