FLock.io’s 1.9B decision model, fine-tuned from decider-2b to apply written policy rules however they are phrased. It returns an option index and a probability in one forward pass for Choice, Noul and Score questions. Weights are open and the model is free to call on the FLock API.
A workable 1.88B-parameter dense decision model from FLock.io. 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.
Access model weights, configuration files, and documentation.
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 | 1.7 GB |
| Acer Veriton GN100 AI MiniAcer | SS | 1.7 GB |
| AMD Instinct MI300XAMD | SS | 1.7 GB |
| AMD Instinct MI325XAMD | SS | 1.7 GB |
| AMD Instinct MI355XAMD | SS | 1.7 GB |

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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 |
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