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Super cool, not sure if there’s already a popular project for this but I’ve seen so many asking for exactly this capability. ‘Conventional’ (if that means anyt
by taneq 3y ago
Super cool, not sure if there’s already a popular project for this but I’ve seen so many asking for exactly this capability.
‘Conventional’ (if that means anything in a field 10 minutes old) wisdom is “fine tune to add knowledge, LoRA to adjust presentation” - could you comment on your experiences with this?
- Havoc 3y agoMy understanding was that RAG is generally a better plan when trying to inject fresh knowledge. Both tuning and LoRA is more about how it responds rather than what it knows
- jerpint 3y agoRAG and LoRA are 2 separate things - I’m RAG, you don’t do any fine tuning , rather you use in context learning
- anon373839 3y agoFine-tuning can also make RAG perform better. You can eliminate the need to waste prompt tokens on n-shot examples, and you can get a much more consistent output for your domain.
- jerpint 3y agoLoRA stands for low rank approximation - it’s a very clever way to fine tune large models with a fraction of the compute, the idea being that you need much less capacity to estimate the finetuning
- taneq 3y agoThanks, yeah I know what LoRA is and I didn't think it was so effective on... are we OK with calling it the 'cognitive' side yet? Everything I've read from the LocalLLaMA finetuning community is that you can fine tune presentation but it's not really an effective way to teach models new knowledge.