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An open re-creation of Jev: a LoRA adapter for Qwen3.5-4B that answers yes/no, Choice (up to 26 options) and Score questions in one pass with calibrated probabilities. The author has since marked this adapter superseded, because the reflex runtime on the frozen base model scores better on unseen data.
A solid 4.66B-parameter dense decision model from kshetrajna12. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing. Newly released, so production-readiness is still being shaken out.
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The top devices for this model at 4-bit, ranked by fit and speed.
| Device | Grade | VRAM |
|---|---|---|
| ACEMAGIC M1A Pro (i9-13900HK + ARC A770)ACEMAGIC | SS | 3.4 GB |
| Acer Veriton GN100 AI MiniAcer | SS | 3.4 GB |
| AMD Instinct MI300XAMD | SS | 3.4 GB |
| AMD Instinct MI325XAMD | SS | 3.4 GB |
| AMD Instinct MI355XAMD | SS | 3.4 GB |
Cheapest current cloud rentals with at least 3 GB VRAM, refreshed hourly.
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| Option | Cost / GPU-hour |
|---|---|
NVIDIA GeForce RTX 4060Vast.ai · Spot · 8 GB VRAM | $0.03 |
NVIDIA GeForce RTX 2080 TiVast.ai · Spot · 11 GB VRAM | $0.04 |
NVIDIA GeForce RTX 3070Vast.ai · Spot · 8 GB VRAM | $0.05 |
NVIDIA GeForce RTX 3060Vast.ai · Spot · 12 GB VRAM | $0.05 |
NVIDIA GeForce RTX 4060 TiVast.ai · Spot · 8 GB VRAM | $0.05 |
Per-GPU rate across RunPod, the Vast.ai marketplace, DigitalOcean, and Vultr.
Spot tier is interruptible. Plan for restarts when comparing against on-demand prices.
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