Re:My finetuned models beat OpenAI's GPT-4
by w4nderlust on Monday July 01, 2024 @03:52PM
(#40849884)
Here is an example of the Predibase platform, referred in the article for the Solar model, but that can train also Llama-3, Phi-3 and Mistral. https://www.youtube.com/watch?v=R2JQhzfaOFw&themeRefresh=1 I think you can assess by yourself if it's easy enough to do for you. (Predibase founder here)
Fine tuning doesn't quite work that way. You have to format the training data set as request/response. The idea of fine tuning is to get the model to output things in a specific format, style or structure.
Your use case is better suited to RAG. This is where you retrieve data from a large dataset and inject it into the user's request so the AI model has the context it needs to answer accurately.
But that's not a silver bullet and you would need to spend significant time on chunking strategy and ranking of results to hopefully get a decent response accuracy.
My use case would be fine tuning on technical docs. Specific news, 2 years of blog posts, primary source material, and Twitter explainer thread. I want to gather all the niche information of a topic from the last two years, dump it into this and have an LLM that is a subject-matter expert.