I have some basic confusions over how to prepare a dataset for training. My plan is to use a model like llama2 7b chat, and train it on some proprietary data I have (in its raw format, this data is very similar to a text book). Do I need to find a way to reformat this large amount of text into a bunch of pairs like “query” and “output” ?
I have seen some LLM’s which say things like “trained on Wikipedia” which seems like they were able to train it on that large chunk of text alone without reformatting it into data pairs - is there a way I can do that, too? Or since I want to target a chat model, I have to find a way to convert the data into pairs which basically serve as examples of proper input and output?
Yea, doing this is part of what spurred the question, because I began to notice some datasets that were very clean and ordered into data pairs, and others that seemed formatted differently, and others still that seemed like they were fed a massive chunk of unstructured text. It made me confused on if there were some sort of standards or not that I was not aware of.