Website: jevstyle.com — all JevStyle decision models, benchmarks and quickstart in one place. A Jev-style decision model: it does not write text. Give it a state, a question and a list of options, and it returns the decision with calibrated probabilities from a single token position. Runs in LM Studio and llama.cpp. MLX bf16 build for Apple Silicon: chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-MLX-bf16 Everything below is measured on data the model never trained on, with the probabilities exactly as the released weights produce them (no post-processing). - Calibrated out of the box. An ECE of 0.017 on 1,500 examples is statistically indistinguishable from a perfectly calibrated model…
Open weights
apache-2.0
Website: jevstyle.com — all JevStyle decision models, benchmarks and quickstart in one place. A Jev-style decision model: it does not write text. Give it a state, a question and a list of options, and one forward pass returns the decision with calibrated probabilities - in 77 ms on an M1 Max. GGUF builds (BF16 / Q80 / Q4KM) for LM Studio and llama.cpp: chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-GGUF Everything below is measured on data the model never trained on, with the probabilities exactly as the released weights produce them (no post-processing). - Calibrated out of the box. An ECE of 0.017 on 1,500 examples is statistically indistinguishable from a perfectly calibrated model…
Open weights
apache-2.0
1.9B parameters
262,144 tokens
mlx
Website: jevstyle.com — all JevStyle decision models, benchmarks and quickstart in one place. A Jev-style decision model for classification, routing and typed choices. Give it a state, a question and a list of options; one prefill returns a selected option with calibrated probabilities. All three files include independently fitted calibration; use runtime temperature 1.0. Agreement is against CUDA merged BF16 on the same frozen 500-decision subset; accuracy is the task-macro average over its real-label examples. Full precision comparison. 81.20% macro accuracy on the fixed English reference panel, compared with 76.68% for v1 and 75.09% for English Laya. The results below use the CUDA…
Open weights
apache-2.0
gguf
Website: jevstyle.com — all JevStyle decision models, benchmarks and quickstart in one place. A Jev-style decision model for classification, routing and typed choices. Give it a state, a question and a list of options; one prefill returns a selected option with calibrated probabilities. Download this build: model.safetensors. The repository also includes its calibration, inference client and evaluation records. 81.20% macro accuracy on the fixed English reference panel, compared with 76.68% for v1 and 75.09% for English Laya. The results below use the CUDA reference structure: 11 real-label task groups, 3,277 decisions, equal task weights, and the same 3,100-record calibration split.…
Open weights
apache-2.0
1.9B parameters
262,144 tokens
mlx
Website: jevstyle.com — all JevStyle decision models, benchmarks and quickstart in one place. A Jev-style decision model for classification, routing and typed choices. Give it a state, a question and a list of options; one prefill returns a selected option with calibrated probabilities. Download this build: model.safetensors. The repository also includes its calibration, inference client and evaluation records. 81.20% macro accuracy on the fixed English reference panel, compared with 76.68% for v1 and 75.09% for English Laya. The results below use the CUDA reference structure: 11 real-label task groups, 3,277 decisions, equal task weights, and the same 3,100-record calibration split.…
Open weights
apache-2.0
1.9B parameters
262,144 tokens
transformers
