Also from my experience you need more power to get some significant result. Mostly fine tuning would work if base model is very close to what you are trying to achieve and you won't be much happy with the results though.
Also context length becomes an issue trying to fit in with gpu with lesser ram.
Maybe I misunderstand, but it seems like they are using LoRa, which is a fine tuning implementation. That requires an already existing trained LLM. If that's true, I think that the title of this submission is inaccurate, as this doesn't let you train a model from scratch with 2 consumer GPUs.
I hand curate github.com/underlines/awesome-ml so I read a ton about latest trends in this space. when I started to read the article, I felt a lot of information was weirdly familiar and almost outdated.
the space is moving fast after all. they just seem to be explaining QLoRA fine tuning, (yes great achievement and all the folks involved are heroes) but reading a trending article on HN - it felt off.
turns out I was too dumb to check the date: 2024 and the title is mixing up quantized adapter fine tuning with base model training. thanks lol
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Also context length becomes an issue trying to fit in with gpu with lesser ram.
the space is moving fast after all. they just seem to be explaining QLoRA fine tuning, (yes great achievement and all the folks involved are heroes) but reading a trending article on HN - it felt off.
turns out I was too dumb to check the date: 2024 and the title is mixing up quantized adapter fine tuning with base model training. thanks lol