When you say Jev is optimized for it, I get that there’s no need for unnecessary decodes in an autoregressive fashion.
But both LLMs and Jev-like models would need to prefill, the only optimization Jev does differently is the decode which can be emulated by reading off logprobs.
We don’t know the param size of Jev, to determine the most comparable model, but if I had to guess it’s sub-100B.
But both LLMs and Jev-like models would need to prefill, the only optimization Jev does differently is the decode which can be emulated by reading off logprobs.
We don’t know the param size of Jev, to determine the most comparable model, but if I had to guess it’s sub-100B.