Jev is such a different approach where you have to be specific about what you want and which options are open. Really interesting how those things evolve in usable features for people.
Also with this example the speed of new launches based on a launch is just incredible.
Not sure on that, maybe the options to choose from will be generated and curated. Same as we do with tagging datasets for images. Might be wildly successful for real world decisions.
I'm confused... This has no relation with the Jev team, isn't it?
It's trying to "emulate" Jev behavior using a regular small LLM model (Qwen3 0.6B or MiniCPM5 2B). And with the smallest model it takes like between half to two seconds to run in my M2 Max, so it's not super fast.
I mean, it's faster than asking to a regular LLM, but I think that's not proper to have Jev on the name (also legally...)
Edit: no shade, and I'll give it a try for some ideas. I'd also like to have an open weights Jev but I think the naming is misguiding. I also have to try Jev that, BTW, got access pretty quickly, less than a day I think...
OP's point here is that the overall approach of restricting output token space and using parallel prompts to produce concurrent results and taking the most relevant ones isn't something novel to Jev (not saying there's nothing novel, but a facsimile can be created at the application layer using any small, fast model)
Jev is such a different approach where you have to be specific about what you want and which options are open. Really interesting how those things evolve in usable features for people.
Also with this example the speed of new launches based on a launch is just incredible.
"... such a different approach where you have to be specific about what you want and which options are open" --- back to where we started ...
Not sure on that, maybe the options to choose from will be generated and curated. Same as we do with tagging datasets for images. Might be wildly successful for real world decisions.
This is true Jevons Paradox (hence the Jev name) there will be so many usecases, applications and even new jobs out of this.
Learned also that Jev was trained on 100%(!) synthetic data.
What a great time to be alive.
As opposed to a fake choice?
I'm confused... This has no relation with the Jev team, isn't it?
It's trying to "emulate" Jev behavior using a regular small LLM model (Qwen3 0.6B or MiniCPM5 2B). And with the smallest model it takes like between half to two seconds to run in my M2 Max, so it's not super fast.
I mean, it's faster than asking to a regular LLM, but I think that's not proper to have Jev on the name (also legally...)
Edit: no shade, and I'll give it a try for some ideas. I'd also like to have an open weights Jev but I think the naming is misguiding. I also have to try Jev that, BTW, got access pretty quickly, less than a day I think...
OP's point here is that the overall approach of restricting output token space and using parallel prompts to produce concurrent results and taking the most relevant ones isn't something novel to Jev (not saying there's nothing novel, but a facsimile can be created at the application layer using any small, fast model)
Correct me if I'm wrong but Jev itself works pretty much the same as encoder only models.
I gave it a choice of "Foo" and "Bar" and it scored "Foo" at 98% percent. Why not 0% for both?
Because it's forced to rate them, there's should be a separate uncertainty parameter for both.
Did you try Tabs and Spaces?
I really hate the way that LLMS design websites.
Unfortunately huggingface.co is blocked by my company's firewall and VPN so it breaks when downloading a model.
Are there any huggingface mirrors out there?