Not only that, but the model was trained on 172 training examples - and the conversations aren't remotely like the therapist conversations I've had in terms of nuance or topic.
I have shared a notebook and explained detailed steps in my blog - https://medium.com/@iamarunbrahma/fine-tuning-of-falcon-7b-l.... If you are interested to replicate the steps on medical-domain therapy chat transcripts, you are definitely welcome. If you face any issues during fine-tuning steps, you can connect with me on my blog. Would love to help out!
Skill issue - focus more on data integrity & less on HN clout
This is Hacker News. These types of projects are half of the reason I come here, what's with the hate?
I wonder if retrieval augmented generation would be appropriate here too. Fast, scalable. Fine-tuning is comparatively pretty expensive.
Thanks for sharing your code, iamarunbrahma.
RAG and fine-tuning serves a slightly different purpose. Fine-tuning helps LLM in learning a new task/skill such as question/answering task, summarization task etc, and improving reliability at producing a desired output such as JSON format structure thereby reducing dependency on prompt engineering.
On the other hand, RAG provides you with external domain-specific knowledge, which one can leverage to get latest information.