@TeksEdge: Someone just made ordinary Qwen behave a LOT more like Jev without training a new model. TOP Jev Clone (according to HF…
Summary
A new technique called JEVfire enables existing LLMs like Qwen to behave more like Jev by modifying decision-making processes without retraining, resulting in significantly faster JSON generation and enabling local AI agents to run efficiently on consumer hardware.
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Cached at: 09/23/26, 04:14 PM
Someone just made ordinary Qwen behave a LOT more like Jev without training a new model.
🏆 TOP Jev Clone (according to HF leaderboard)
🎯JEVfire takes an existing open LLM and changes how it makes decisions.
Instead of prompt → generate { → key → : → value → comma → …
It gives Qwen a fixed menu of legal answers, reads the model’s scores, picks the winners, and builds the JSON in code.
No retraining. No second model. No autoregressive JSON serialization.
On Qwen3.8-27B-FP8 + vLLM … 4 decisions 🐌 JSON generation: 878 ms ⚡ JEVfire: 110 ms 🚀 8× faster
28 decisions: 🐌 JSON: 5.11 sec ⚡ JEVfire: 497 ms 10.3× faster
With a cached prefix … 🚀 346 ms 👉 14.8× faster
They put Qwen3.5-0.8B in a browser with WebLLM/WebGPU and had it make real-time game decisions locally. 🍄 Mario-style demo 🧠 ~450MB model 💾 ~2GB free GPU/unified memory ⚡ 71 ms/model decision on an M4 Max ☁️ no inference server 🔑 no API key
This is NOT TypeSafe’s Jev and it doesn’t reproduce Jev’s RLCD training.
But the idea is fascinating! Maybe making Local AI agents fast isn’t only about smaller models.
🔗 Link in ALT
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