I turned Jev into a (lousy) chatbot(github.com) |
I turned Jev into a (lousy) chatbot(github.com) |
> a story
I think this is the first time I’ve knowingly laughed at a model’s joke.
An early ChatGPT model made me a pretty funny track list for my imaginary Indian cover band The Needful Dead. It's no longer in my chat history though so clearly Sam retconned anything his models did that could be considered racist.
When it first came out, I thought "this weekend, I'll do a little open-source Jev based on single-token prediction and the token logit output", but of course when it came to it, there were at least 5 that had already been done between me thinking that and getting around to it.
So I wrote up[0] what other people had done, but wasn't happy with how weak the benchmarks were, but in the time between writing the first word and the last few, two excellent sets of benchmarks had been written, so I was able to incorporate those. I published the article, and one of the authors of one of the implementations commented that I'd beaten him to doing the write-up he'd wanted to.
This morning I thought "huh, you could have some fun giving Jev a single letter or token at a time, turning it into a chatbot", but as the time of looking two people had already done this (and taken the gag further than I would have), and ... this is isn't either of the ones I'd found. I bet if you scratch the surface there already at leat 5.
Time from idea to output has dropped off a fucking cliff.
How is this idea generally working out in comparison with Jev? I'm curious, from what I read so far it seems like Jev is still beating this kind of thing.
But it's curious because it's not entirely clear why, from an architecture point of view for all we know that's exactly what they're doing. So it must come down to the quality of those logits, ie., model size and training details.
It seems to me that what most of these single-token-prediction projects are missing is that Jev seems to be claiming they predict well-calibrated probabilities. This is an incredibly valuable thing that LLMs simply can't deliver unless they are trained specially for it.
Jev clearly has _some_ secret sauce compared to doing the dumbest thing that could possibly work with Qwen. It's not clear how durable that advantage is against OpenAI wiring up Luna-5.6 and doing a minimum amount of tweaking, but I presume we'll know in a week or two.
I think the real difference Jev makes is the fast parallel decode, it just seems rather bizzare how that works.
https://docs.typesafe.ai/model-jaggedness/jev-1.13#generatio...
Slack is glorified IRC yet they're worth billions.
Dropbox can be trivially implemented via rsync yet they're worth billions.
I quite enjoyed handwriting my article to be honest.
Similar to how we upgraded computers for decades and the software bloated to fill the specs