Very interesting insight on the training process, it's pretty cool to have some experimental justification for why they took these exact steps, what they tried and did not work, etc. Feels a bit less like dark magic.
However I agree the latency argument doesn't hold much value with Jev because it runs on a remote server. Seeing how many open Jev-like models came out recently it would be much more interesting to have a comparison with them.
You're comparing a local GPU to network hops? Wouldn't be surprised if Jev was actually similar in runtime and their is just a great deal of network latency.
The evaluation is quite interesting though - I'd actually say the raw answer is correct in the absence of detail and prior knowledge (Who wrote the play Romeo and Juliet).
what's with that dino run? Jev is slow but it jumps correctly, their model always touches the cactus or whatever it is...I am guessing it doesn't matter? Or does it?
I actually explored this for robotics last year, and CLM grew directly out of that work a few months ago. Feel free to check out my earlier post for more details on how CLM could be applied to robotics:
https://x.com/jackyk02/status/2026368947210289660
However I agree the latency argument doesn't hold much value with Jev because it runs on a remote server. Seeing how many open Jev-like models came out recently it would be much more interesting to have a comparison with them.
You're comparing a local GPU to network hops? Wouldn't be surprised if Jev was actually similar in runtime and their is just a great deal of network latency.
The evaluation is quite interesting though - I'd actually say the raw answer is correct in the absence of detail and prior knowledge (Who wrote the play Romeo and Juliet).
https://x.com/jackyk02/status/2102947585208258799?s=20
But of course the repo with the TypeSafe compatible API is new: https://github.com/Contrastive-LM/CLM