Decily scores a runtime-provided set of candidates against a state and a question, and returns a calibrated probability distribution over those candidates. It does not generate text. Decily is built for decisions where you want a probability you can threshold, not a sentence you have to parse. ONNX int8 build (1.66 GB, single self-contained file) — the CPU/edge artifact. Dynamic int8 quantization of the full scoring graph (inputids + attentionmask -> logits); PyTorch is not needed to run it. Each row is one candidate's state + question + candidate token sequence; the logits are softmaxed over the candidates. Reference runner: fair-suite parity batch; the fp32 graph matches (Apple M5 CPU, 4…
Independent publisher
Alexzhang
alexzhang0118
Models in Library2
Datasets in Library0
Models on Hugging Face4
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Models
Decily scores a runtime-provided set of candidates against a state and a question, and returns a calibrated probability distribution over those candidates. It does not generate text. Decily is built for decisions where you want a probability you can threshold, not a sentence you have to parse. Apple MLX 4-bit build — the on-device variant. The tower is quantized (group-64 affine, 968 MB); the scoring head ships separately in decisionhead.safetensors (kept in fp32 for calibration). This card covers all four released artifacts; they are the same model in different containers. state + question + candidate jointly; an attention-pooling layer over all tokens feeds a LayerNorm/GELU MLP that…