I am actively testing this out. It’s hard to say at the moment. There’s a lot to figure out deploying a model into a live environment, but I think there’s real value in using them for technical tasks - especially as models mature and improve over time.
At the moment, though, performance is closer to GPT 3.5 than GPT 4, but I wouldn’t be surprised if this is no longer the case within the next year or so.
Assuming everything from the papers translate into current platforms, yes! A rather significant one at that. Time will tell us the true results as people begin tinkering with this new approach in the near future.
Thanks for reading! I’m glad you enjoy the content. I find this tech beyond fascinating.
Who knows, over time you might even begin to pick up on some of the nuance you describe.
We’re all learning this together!
Thanks for sharing this!
Good bot, I will do that next time.
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I run this show solo at the moment, but do my best to keep everyone informed. I have much more content on the horizon. Would love to have you if we have what you’re looking for.
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I used to feel the same way until I found some very interesting performance results from 3B and 7B parameter models.
Granted, it wasn’t anything I’d deploy to production - but using the smaller models to prototype quick ideas is great before having to rent a gpu and spend time working with the bigger models.
Give a few models a try! You might be pleasantly surprised. There’s plenty to choose from too. You will get wildly different results depending on your use case and prompting approach.
Let us know if you end up finding one you like! I think it is only a matter of time before we’re running 40B+ parameters at home (casually).