APUS-OpenJev

4B, 9B, and 35B-A3B decision family for browser agents. ~26ms median. TypeSafe SDK. Weights on Hugging Face.

by @shootime007Added Sep 22, 2026

🚀 APUS-OpenJev: I built a 4B/9B/35B-A3B model family for browser agents and business workflows. ⚡ ~26 ms median response 🎯 82.2% vs Jev’s 77.0% 🧠 Selectable compute depth 🔌 TypeSafe SDK integration Models (huggingface.co/collections/ap…)

4:29 AM · Sep 22, 2026

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Oliver Yu

@oliver_yu9

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Everyone’s making Laya vs Jev game demos, so I tried a different experiment: Can the same Laya model run on both the MLX GPU and Apple Neural Engine? Then I used both at once for serving. M4 Max, bursty load: P99 1592.9 ms → 79.5 ms github.com/tc3oliver/laya…

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3:46 AM · Sep 24, 2026

laya-apple

Same Laya weights on MLX GPU and Apple Neural Engine at once. P99 1593ms to 80ms on M4 Max.

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Hemant

@heman10x

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Did my openJev Verdict(Oss jev made on just 151M param base model) hit #2(possibly #1 even?) on JevBenchmarks> When JevBench v1.2 evaluated openJev Verdict earlier this week, we were sitting at #11 with a score of 66.2. I audited the benchmark run this morning and realized the Show more

Hemant

@heman10x

Did my OSS-Jev called Verdict, beat Official Typesafeai 's Jev ?, and Laya (the best performing OSS Jev) on their own benchmark? When I first tested Verdict 1.0(my earlier attempt of oss-jev) earlier this week, it scored 26.10% accuracy. Uniform random guessing on that split is

3:25 PM · Sep 20, 2026

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Xjevopen source

Sep 22, 2026

openJev Verdict

Open 151M decision model on ModernBERT. JevBench #2 on public tasks after the inference-engine fix. Weights and a WebGPU playground.

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Nandakishor m

@Nandakishorm1

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Asking everyone to fine tune github.com/NandhaKishorM/… Just use any kinda agentic harness like claude code, codex etc. Change the code and use free kaggle GPUs to train your own use cases. Base model has limitations

10:21 AM · Sep 21, 2026

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Xjevopen source

Sep 22, 2026

Laya

Open Apache 2.0 System One model. ModernBERT, typed choice/score/noul in one pass. Fine-tune notebook on Kaggle T4s. Not Jev.

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Jared Palmer

@jaredpalmer

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Kev has now been refactored on top of Qwen3.5. New checkpoints are now available at 0.8B, 4B, and 9B along with a fine-tuning script you can use with @modal. github.com/jaredpalmer/kev

Jared Palmer

@jaredpalmer

UPDATE: Kev-0.6B, 4B, and 8B are now available. Kev is a family of small open source Jev-like decision models you can train and run yourself. This new family is based on Qwen3 using the same LoRA + small pointer head technique as before, but scaled up. Out of domain, on data

2:52 PM · Sep 21, 2026

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Xjevopen source

Sep 22, 2026

Kev

Jared Palmer's open Jev-like family on Qwen3.5. 0.8B, 4B, 9B. TypeSafe SDK with a base_url change. Train on one H100.

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