Turns Jev into a chatbot. Contribute to kyle-pena-nlp/jevchat development by creating an account on GitHub.

177 points•kp1197•10 days ago•50 comments•

50 comments

ericpruitt10 days ago
It's the digital equivalent of Morty speaking with the death crystal: https://youtu.be/YjepJlvkdKs?t=51. The crystal shows him how he will die, so he iteratively determines his speech based on whether he sees himself dying with the life he wants.
written-beyond10 days ago
Sometimes I think I'm Morty speaking with the death crystal but I don't even have a death crystal and all I fear is life itself.
doginasuit10 days ago
We all have a death crystal, it's just wrong sometimes. It may confuse a certain possibility with death, when in reality it is a temporary discomfort or setback.

If you ignore it and you survive, it eventually recalculates and you stop seeing that future.

OtherShrezzing10 days ago
>> write me a short story

> a story

I think this is the first time I’ve knowingly laughed at a model’s joke.

phoghed10 days ago
In the olden days when the text-davinci models would just wholesale make shit up they did some pretty funny completions.

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.

zamadatix10 days ago
Reminds me of the very early LlMs. I also sometimes miss the sheer demented horror of early image models told to generate images of biology.
solarkraft10 days ago
Good times. My best remembered examples are the bottomless pit supervisor (https://knowyourmeme.com/memes/bottomless-pit-supervisor) and the model being told to generate a screaming hot soup.
boodleboodle10 days ago
We’ve come full circle to “BERT has a mouth and it must speak” https://arxiv.org/abs/1902.04094
IceDane10 days ago
The worst part about this is someone still using poetry for python development.
petesergeant10 days ago
Jev has taught me the same lesson three times over now.

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.

0: https://sgnt.ai/p/jev/

radarsat110 days ago
> I'll do a little open-source Jev based on single-token prediction and the token logit output

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.

petesergeant10 days ago
> How is this idea generally working out in comparison with Jev?

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.

applfanboysbgon10 days ago
Time from pointless idea to bad output, anyways. We aren't seeing good software, and now neat hobby project ideas are getting harvested pointlessly when the only purpose of those ideas was the fun and learning of doing.
margalabargala10 days ago
Good ideas are the survivors of lots of bad ideas.

Slack is glorified IRC yet they're worth billions.

Dropbox can be trivially implemented via rsync yet they're worth billions.

cyanydeez10 days ago
LLMs are now like major highways, and everyone thinks theyll solve software jams by just adding one more lane; but that just induces demand, and doesnt increase efficiency because the traffic jam is about how people evaluate usage and fill the voids.

Similar to how we upgraded computers for decades and the software bloated to fill the specs

petesergeant10 days ago
> when the only purpose of those ideas

I quite enjoyed handwriting my article to be honest.

moffkalast10 days ago
Everything old is new again, huh. I remember people doing this back in the early llama days, restricting grammars to yes and no tokens or 0 and 1 and then classifying questions. Usually it was rather ass in terms of performance cause no model is tuned to reply that way and it was WAY out of distribution, and yet then it got turned into the main way to run multiple choice benchmarks, and then everyone benchmaxxed it. Doesn't the normal MMLU/Pro also just do the same thing, restrict the output to one token, top-k=1, and it has to be one of the choice letters?

I think the real difference Jev makes is the fast parallel decode, it just seems rather bizzare how that works.

jorl1710 days ago
I had the same sort of thing going on ahah. But I was convinced some hoje must have already done it and I decided I’d research when I got home (I’m out today). Didn’t expect it to reach hacker news so soon, though!!
digilypse10 days ago
I tried this too but it’s literally addressed in their docs already so I don’t think it’s much of a discovery

https://docs.typesafe.ai/model-jaggedness/jev-1.13#generatio...

genxy9 days ago
What will you think up next?

Maybe you are conjuring them into existence.

Read the full thread on Hacker News →

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