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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.
Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.
Access model weights, configuration files, and documentation.
Benchmark data sourced from: Hugging Face, Arena.ai, Artificial Analysis.
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 |
| AI TOP ATOM ATAGB10-9003Gigabyte | SS | 3.4 GB |
| AMD Instinct MI300XAMD | SS | 3.4 GB |
| AMD Instinct MI325XAMD | SS | 3.4 GB |
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Cheapest current cloud rentals with at least 3 GB VRAM, refreshed hourly.
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| Option | Cost / GPU-hour |
|---|---|
NVIDIA GeForce RTX 3060Vast.ai · Spot · 12 GB VRAM | $0.04 |
NVIDIA GeForce RTX 3060Vast.ai · On-Demand · 12 GB VRAM | $0.04 |
NVIDIA GeForce RTX 3080 TiVast.ai · Spot · 12 GB VRAM | $0.06 |
NVIDIA GeForce RTX 5060 TiVast.ai · Spot · 16 GB VRAM | $0.07 |
NVIDIA GeForce RTX 3090Vast.ai · Spot · 24 GB VRAM | $0.07 |
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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