Typesafe has been quite about the underlying technology behind Jev. Given the speed and cost my hypothesis is that it doesn’t input tokens the way that LLMs do, ie iterating over every word and drawing the connections between each. That is an o(n^2) problem which is why LLMs are so expensive as they scale.
If I had to guess, it's already built and is just waiting on Product's/Marketing's desk. How do you position this without looking like your roadmap is being determined by newcomers? Probably don't want to adopt the same verbiage+acronyms - but also can't be seen to be just sherlocking features.
For _your_ classification it's unacceptable. The OP seems to have anticipated this and mentions you can fine tune it for your use case. Did you try that?
I don't think the point is to displace Jev, but to show it's possible to build an MVP on open weights without years of work and millions of dollars.
Why (presumably) an engineer would dismiss exploring a lightweight, custom alternative to locking into a fashionable PaaS, I'll never know.
This type of project looks extremely useful. There was a lot of buzz around Jev, but having models that run locally and can be fine-tuned is extremely helpful.
Oh wow, those Doom scores for Jeff are pretty terrible
The Von numbers have led me on a rabbit whole of getting a classifier to play Doom
I got it to average 22 kills (the max is 26) on that same scenario that Jeff and Von are testing on (it’s called Defend Center)
Now I’m having it play a more advanced scenario, and it’s doing about 45 kills (SOTA is ~59 kills)
It’s amazing what you can do with small classifiers if you can collect some data. These models I’m testing train on CPU in seconds (what takes the longest is running the game, doing test runs and collecting data), they are <1MB in size and do inference in <1ms on CPU
Hi HN. Jeff is a set of small, open-weight Qwen3.5 and Gemma fine-tunes for zero-shot classification, with respectable out-of-the-box performance, meant to be slotted right into code (or fine-tuned further as needed). You give them a situation and a list of options; they return a calibrated probability for each, in one forward pass, with no text generation. The 2B scores 83.1% on a five-benchmark panel (Jev's published figure: 83.0%); the 0.8B decides in about 28 ms on an M4 Max. Apache 2.0, with a Jev-compatible API (I'm not affiliated with TypeSafe).
When TypeSafe released Jev a couple of weeks ago and then AutoJev appeared, I wanted to see if I could replicate the experiment using only small language models on local hardware. So everything ran at home: one RTX PRO 6000 for training, two DGX Sparks running Qwen3.8-Flash-Next to write the synthetic data, a MacBook for testing, all monitored from my phone over Tailscale.
The caveat: the published Jev and AutoJev numbers are on a different sample of the same benchmarks, and Jeff's overall score comes from classification-style tasks (96% on Financial PhraseBank, 86-89% on RAGTruth, both above Jev). On multi-step reasoning it's behind: BBH 64-68% against Jev's 94%, and about 50% on JevBench's hard tier against 73%. That isn't surprising, and I don't think it matters: no 0.8B or 2B model reasons like a large one, and nobody should expect it to. These are extremely fast judgement-callers. In one of my apps I use the 0.8B for voice navigation; a quick fine-tune (about half an hour on one GPU) took it from 32% to 96% on held-out commands, at about 40 ms per decision.
The fun part: games, as a zero-shot test. Games aren't the ideal zero-shot test, but they're fun, and TypeSafe did it with Jev too. There was no game data in training. Each turn the code describes the situation and the moves in words, and the model picks one; the options say what each move leads to, never which one is right. Over 20 episodes each:
- Doom (ViZDoom): Jeff 0.8B 6.55 kills per episode, the same as a hand-coded bot and as Jev's published run. Jev's prompt spells out the aiming rule and takes about 212 ms per call over its API; Jeff gets "the nearest monster is a little to your left" and decides in about 29 ms on my Mac.
- Frogger: 10.3 crossings, level with the hand-coded bot (10.25), and 10x the untrained base model (1.0).
- Pac-Man: 57 of 98 pellets, about 60% of the bot's score and 2x the untrained model.
Videos of every run are linked in the README.
Lessons learned:
- System 1 models are here to stay. Being able to process unstructured data at software speed inside an app is extremely powerful, and being able to do it locally is fantastic.
- A small model is a classifier, not a planner. Models of 0.8B-2B don't reason like Qwen3.8-27B or Jev, and they don't need to: present the options the right way and you get 40+ decisions per second, depending on your hardware.
- Fine-tune it if needed. If zero-shot isn't good enough for your task, a short fine-tune on your own examples is.
- Wording matters enormously. Giving Frogger's final step the same words as every other forward option ("safe, and one row closer to the goal") took one episode from 15 crossings to 23. Before that, the frog just stayed on the last log.
- Bigger isn't better. The untrained 2B is already more risk-averse than the untrained 0.8B (in Doom it prefers turning away from the nearest monster), and training made it hesitate in Pac-Man. That's probably why the 0.8B beat the 2B.
- Benchmarks don't predict play. Untrained Gemma 4 E2B beats both untrained Qwens on the benchmarks (62.5%) and plays every game worst: right most of the time, but not reliably, and in a real-time loop the mistakes compound.
Funny that the 2B loses to the 0.8B. Question about the benchmarks: BBH and JudgeBench are more reasoning, where you fall behind, but for zero-shot classification there are more relevant ones like Banking77 or CLINC150. Was there no temptation to pick something closer to where System 1 models are actually used?
Typesafe has been quite about the underlying technology behind Jev. Given the speed and cost my hypothesis is that it doesn’t input tokens the way that LLMs do, ie iterating over every word and drawing the connections between each. That is an o(n^2) problem which is why LLMs are so expensive as they scale.
Forgive my lack of understanding but how long before Jev type functionality is just built straight into all frontier models?
If I had to guess, it's already built and is just waiting on Product's/Marketing's desk. How do you position this without looking like your roadmap is being determined by newcomers? Probably don't want to adopt the same verbiage+acronyms - but also can't be seen to be just sherlocking features.
No need, as that functionality can run locally no problem.
BeRT and FLAN-T5 were used as classifiers 5-7 years ago, they were technically "frontier" for their time.
I compared it to Jev in my current use cases and it's very inaccurate. 70% vs 94% . for classification, it's unacceptable.
For _your_ classification it's unacceptable. The OP seems to have anticipated this and mentions you can fine tune it for your use case. Did you try that?
I don't think the point is to displace Jev, but to show it's possible to build an MVP on open weights without years of work and millions of dollars.
Why (presumably) an engineer would dismiss exploring a lightweight, custom alternative to locking into a fashionable PaaS, I'll never know.
[delayed]
This type of project looks extremely useful. There was a lot of buzz around Jev, but having models that run locally and can be fine-tuned is extremely helpful.
Von 1.2 had a better Doom score :D
https://github.com/wfzyx/von
Oh wow, those Doom scores for Jeff are pretty terrible
The Von numbers have led me on a rabbit whole of getting a classifier to play Doom
I got it to average 22 kills (the max is 26) on that same scenario that Jeff and Von are testing on (it’s called Defend Center)
Now I’m having it play a more advanced scenario, and it’s doing about 45 kills (SOTA is ~59 kills)
It’s amazing what you can do with small classifiers if you can collect some data. These models I’m testing train on CPU in seconds (what takes the longest is running the game, doing test runs and collecting data), they are <1MB in size and do inference in <1ms on CPU
Can we get a price comparisson ?
Edit: Running them for the masses.
Hi HN. Jeff is a set of small, open-weight Qwen3.5 and Gemma fine-tunes for zero-shot classification, with respectable out-of-the-box performance, meant to be slotted right into code (or fine-tuned further as needed). You give them a situation and a list of options; they return a calibrated probability for each, in one forward pass, with no text generation. The 2B scores 83.1% on a five-benchmark panel (Jev's published figure: 83.0%); the 0.8B decides in about 28 ms on an M4 Max. Apache 2.0, with a Jev-compatible API (I'm not affiliated with TypeSafe).
When TypeSafe released Jev a couple of weeks ago and then AutoJev appeared, I wanted to see if I could replicate the experiment using only small language models on local hardware. So everything ran at home: one RTX PRO 6000 for training, two DGX Sparks running Qwen3.8-Flash-Next to write the synthetic data, a MacBook for testing, all monitored from my phone over Tailscale.
The caveat: the published Jev and AutoJev numbers are on a different sample of the same benchmarks, and Jeff's overall score comes from classification-style tasks (96% on Financial PhraseBank, 86-89% on RAGTruth, both above Jev). On multi-step reasoning it's behind: BBH 64-68% against Jev's 94%, and about 50% on JevBench's hard tier against 73%. That isn't surprising, and I don't think it matters: no 0.8B or 2B model reasons like a large one, and nobody should expect it to. These are extremely fast judgement-callers. In one of my apps I use the 0.8B for voice navigation; a quick fine-tune (about half an hour on one GPU) took it from 32% to 96% on held-out commands, at about 40 ms per decision.
The fun part: games, as a zero-shot test. Games aren't the ideal zero-shot test, but they're fun, and TypeSafe did it with Jev too. There was no game data in training. Each turn the code describes the situation and the moves in words, and the model picks one; the options say what each move leads to, never which one is right. Over 20 episodes each:
- Doom (ViZDoom): Jeff 0.8B 6.55 kills per episode, the same as a hand-coded bot and as Jev's published run. Jev's prompt spells out the aiming rule and takes about 212 ms per call over its API; Jeff gets "the nearest monster is a little to your left" and decides in about 29 ms on my Mac.
- Frogger: 10.3 crossings, level with the hand-coded bot (10.25), and 10x the untrained base model (1.0).
- Pac-Man: 57 of 98 pellets, about 60% of the bot's score and 2x the untrained model.
Videos of every run are linked in the README.
Lessons learned:
- System 1 models are here to stay. Being able to process unstructured data at software speed inside an app is extremely powerful, and being able to do it locally is fantastic.
- A small model is a classifier, not a planner. Models of 0.8B-2B don't reason like Qwen3.8-27B or Jev, and they don't need to: present the options the right way and you get 40+ decisions per second, depending on your hardware.
- Fine-tune it if needed. If zero-shot isn't good enough for your task, a short fine-tune on your own examples is.
- Wording matters enormously. Giving Frogger's final step the same words as every other forward option ("safe, and one row closer to the goal") took one episode from 15 crossings to 23. Before that, the frog just stayed on the last log.
- Bigger isn't better. The untrained 2B is already more risk-averse than the untrained 0.8B (in Doom it prefers turning away from the nearest monster), and training made it hesitate in Pac-Man. That's probably why the 0.8B beat the 2B.
- Benchmarks don't predict play. Untrained Gemma 4 E2B beats both untrained Qwens on the benchmarks (62.5%) and plays every game worst: right most of the time, but not reliably, and in a real-time loop the mistakes compound.
Funny that the 2B loses to the 0.8B. Question about the benchmarks: BBH and JudgeBench are more reasoning, where you fall behind, but for zero-shot classification there are more relevant ones like Banking77 or CLINC150. Was there no temptation to pick something closer to where System 1 models are actually used?
That's some local hardware.