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A research method on a frozen Qwen3-4B-Instruct-2507 that answers closed-choice fields by reading the scores of option letters instead of generating JSON. Its preregistered study found the same accuracy as grammar-constrained JSON with no decoding cost. It covers Choice and yes/no questions with up to 26 options.
A situational 4.02B-parameter dense decision model from r-ms. 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.
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 | 3.0 GB |
| Acer Veriton GN100 AI MiniAcer | SS | 3.0 GB |
| AMD Instinct MI300XAMD | SS | 3.0 GB |
| AMD Instinct MI325XAMD | SS | 3.0 GB |
| AMD Instinct MI355XAMD | SS | 3.0 GB |
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NVIDIA GeForce RTX 5060 TiVast.ai · Spot · 16 GB VRAM | $0.08 |
NVIDIA RTX A4000Vast.ai · Spot · 16 GB VRAM | $0.08 |
NVIDIA Tesla V100 16GBVast.ai · Spot · 16 GB VRAM | $0.09 |
NVIDIA RTX A4000Vast.ai · On-Demand · 16 GB VRAM | $0.09 |
NVIDIA GeForce RTX 5060 TiVast.ai · On-Demand · 16 GB VRAM | $0.11 |
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