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EveYoung | 2 years ago

In my experience, the threshold to be useful is much lower than GPT-3.5. These smaller models can "easily" be finetuned to achieve a comparable performance on a specific task. For example, I've achieved promising results for data summarisiation and image captioning (BLIP2-based) using Alpaca.

Also, server/hardware costs are still a limiting factor for running and finetuning the larger 33/65B Llama models. Especially, if they can only be used for personal toy projects.

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bugglebeetle|2 years ago

I don’t use LLMs for anything image related, so I can’t speak to their value there, but almost all simpler NLP tasks are IMO better handled using other techniques that predate them. I’ve yet to see an example where fine-tuning is cheaper/more efficient/better performing than older solutions to these problems.

EveYoung|2 years ago

If older techniques work for you, there is of course no reason to switch to LLMs besides general curiousity or to explore what's possible already. That said, in my case I was enable to generate much more engaging text summaries of tabular data using a Llama derivative.