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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 au
by 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 agoThe 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 agoI 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.