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I think the weird thing about this is that it's completely true right now but in X months it may be totally outdated advice.
For example, efforts like OpenMOE https://github.com/XueFuzhao/OpenMoE or similar will probably eventually lead to very competitive performance and cost-effectiveness for open source models. At least in terms of competing with GPT-3.5 for many applications.
Also see https://laion.ai/
I also believe that within say 1-3 years there will be a different type of training approach that does not require such large datasets or manual human feedback.
I think the weird thing about this is that it's completely true right now but in X months it may be totally outdated advice.
For example, efforts like OpenMOE https://github.com/XueFuzhao/OpenMoE or similar will probably eventually lead to very competitive performance and cost-effectiveness for open source models. At least in terms of competing with GPT-3.5 for many applications.
Also see https://laion.ai/
I also believe that within say 1-3 years there will be a different type of training approach that does not require such large datasets or manual human feedback.
Eh, OpenAI is too cheap to beat at their own game.
But there are a ton of use-cases where a 1 to 7B parameter fine-tuned model will be faster, cheaper and easier to deploy than a prompted or fine-tuned GPT-3.5-sized model.
In fact, it might be a strong statement but I'd argue that most current use-cases for (non-fine-tuned) GPT-3.5 fit in that bucket.
(Disclaimer: currently building https://openpipe.ai; making it trivial for product engineers to replace OpenAI prompts with their own fine-tuned models.)
> Does android even have native OCR?
Tesseract? https://github.com/tesseract-ocr/tesseract