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A rank-16 LoRA adapter for Qwen3.5-4B built for policy decisions over a short document. It returns calibrated probabilities for yes/no, Choice and Score questions in one forward pass and serves the JevBench and TypeSafe request format. The weights are for research and demo use only because of training data terms.
A workable 4.66B-parameter dense decision model from HopitAI. 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.
Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.
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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 Tesla V100 16GBVast.ai · Spot · 16 GB VRAM | $0.05 |
NVIDIA GeForce RTX 3090Vast.ai · Spot · 24 GB VRAM | $0.07 |
NVIDIA GeForce RTX 5060 TiVast.ai · Spot · 16 GB VRAM | $0.07 |
NVIDIA GeForce RTX 4060 TiVast.ai · Spot · 8 GB VRAM | $0.07 |
NVIDIA GeForce RTX 4070Vast.ai · Spot · 12 GB VRAM | $0.08 |
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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