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AuspiciousApple OP t1_itwvq1f wrote

>I dont think any model you can run on a single commodity gpu will be on par with gpt-3.

That makes sense. I'm not an NLP person, so I don't have a good intuition on how these models scale or what the benchmark numbers actually mean.

In CV, the difference between a small and large model might be a few % accuracy on imagenet but even small models work reasonably well. FLAN T5-XL seems to generate nonsense 90% of the time for the prompts that I've tried, whereas GPT3 has great output most of the time.

Do you have any experience with these open models?

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_Arsenie_Boca_ t1_ityccjh wrote

I dont think there is a fundamental difference between cv and nlp. However, we expect language models to be much more generalist than any vision model (Have you ever seen a vision model that performs well on discriminative and generative tasks across domains without finetuning?) I believe this is where scale is the enabling factor.

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