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This is a toy project. Hence, large-scale collection of data and pre-processing them into conversational format, also removing sensitive information is not poss

This is a toy project. Hence, large-scale collection of data and pre-processing them into conversational format, also removing sensitive information is not possible. I was able to curate only 172 rows of data. I have shared a notebook and explained detailed steps in my blog - https://medium.com/@iamarunbrahma/fine-tuning-of-falcon-7b-large-language-model-using-qlora-on-mental-health-dataset-aa290eb6ec85 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!

thuuuomas 3y ago
> I was able to curate only 172 rows of data Skill issue - focus more on data integrity & less on HN clout
- drsamuel507 3y ago
  Oh!! So, you want every individual who post their github repo on HN; to collect dataset like billions of dollars funded startups and make a reliable chatbot which not a single well funded startup or listed NASDAQ companies could have done. I salute you.
- SamPatt 3y ago
  OP is sharing a personal project, he's not claiming he's solved the mental health crisis. This is Hacker News. These types of projects are half of the reason I come here, what's with the hate?
bpiche 3y ago
It is interesting. Sorry to pour so much cold water on the topic, it's more the application than the code that people have issue with. 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.
- iamarunbrahma 3y ago
  The purpose of this project was to showcase how can we fine-tune large models on a free-tier GPU provided by Colab. Hence, any individual can utilize parameter-efficient fine-tuning methods to tune LLMs on domain-specific datasets. 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.
  - bpiche 3y ago
    I see, thank you for the explanation. RAG is also appropriate for question/answering but I do see your point. Can see myself returning to your code for inspiration soon.

thuuuomas 3y ago

> I was able to curate only 172 rows of data Skill issue - focus more on data integrity & less on HN clout

drsamuel507 3y ago
Oh!! So, you want every individual who post their github repo on HN; to collect dataset like billions of dollars funded startups and make a reliable chatbot which not a single well funded startup or listed NASDAQ companies could have done. I salute you.
SamPatt 3y ago
OP is sharing a personal project, he's not claiming he's solved the mental health crisis. This is Hacker News. These types of projects are half of the reason I come here, what's with the hate?

bpiche 3y ago

It is interesting. Sorry to pour so much cold water on the topic, it's more the application than the code that people have issue with. 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.

iamarunbrahma 3y ago
The purpose of this project was to showcase how can we fine-tune large models on a free-tier GPU provided by Colab. Hence, any individual can utilize parameter-efficient fine-tuning methods to tune LLMs on domain-specific datasets. 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.
- bpiche 3y ago
  I see, thank you for the explanation. RAG is also appropriate for question/answering but I do see your point. Can see myself returning to your code for inspiration soon.