I don't understand how this is different from oai "structured output" (and whatever the similar paradigm was on Sonnet ~3.7 back then) which everyone moved on from. On their gh they say:
"Jev is TypeSafe's closed service for runtime-defined semantic decisions. This project reproduces that interface pattern with open models; it does not reproduce Jev's undisclosed model or training"
As someone else pointed out it isn't actually Jev... can someone enlighten me
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)
"Customer wants to lear how to better talk in a company situation, and bring across their argument effectively"
Than had it choose what training would be fitting for this user:
- Communication and Feedback
- Leadership for Begninners
- Soft Skills and Emotional Awareness
It picked always the third with an 80% confidence, while the answer should have been 1.
You sure the answer should have been 1? As a human I'd say I don't have enough information to answer this confidently, but "argument effectively" strongly suggests soft skills to me
The same people who are likely seeing tens of the same sort of pages and immediately closing them because "who cares".
I mean I guess I'm looking at this too. But at this point the most interesting projects in the world to me are ones with bad CSS.
Clear and to the point.
If you meant the text then I agree.
"Jev is TypeSafe's closed service for runtime-defined semantic decisions. This project reproduces that interface pattern with open models; it does not reproduce Jev's undisclosed model or training"
As someone else pointed out it isn't actually Jev... can someone enlighten me
Also with this example the speed of new launches based on a launch is just incredible.
Are there any huggingface mirrors out there?
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...
As opposed to a fake choice?
"Customer wants to lear how to better talk in a company situation, and bring across their argument effectively"
Than had it choose what training would be fitting for this user: - Communication and Feedback - Leadership for Begninners - Soft Skills and Emotional Awareness
It picked always the third with an 80% confidence, while the answer should have been 1.
Learned also that Jev was trained on 100%(!) synthetic data.
What a great time to be alive.