Jev Quantization

The NVFP4 build of AutoTrust's JEV-35B: routed experts quantized to 4-bit cut the download to 25.6 GB and GPU weights to 23.3 GiB, keeping 96.6% of bf16's Decision Index answers.

Reza2kn's llama.cpp Q4_K_M build of Jev-Omni: a 6.87 GiB multimodal decision backbone plus projector and FP32 head, measured at 8.98 GiB VRAM on an RTX 5080 Laptop GPU.

Ruiruiz30's Apple Silicon MLX conversion of Jev-Omni: 4-bit affine language weights on a Mac mini M4 16GB, about 7 GB peak Metal memory and roughly 963 ms text decisions, Apache-2.0.

GGUF builds of the JevK5 family for llama.cpp: Q8_0 is effectively lossless (229 of 231 answers match the 4B v0.3 bf16), and every published file clears the card's 95% agreement gate.

The GGUF edition of TokenRhythm's NeoHorse-Jev-4B: BF16 through Q2_K quantizations that each bundle the language backbone, visual weights, decision head, and tokenizer, plus a shared runtime for text and single-image requests.

Vision-capable GGUF port of autotrust/JEV-27B-VL: BF16 through Q3_K_M with a separate mmproj file, running the calibrated System 1 typed-decision interface and a 27B System 2 through llama.cpp.

GGUF quantization of AutoTrust's JEV-9B Blocks-of-Experts model: eight files from BF16 17.9 GB down to Q3_K_M 4.62 GB, wrapping the frozen Qwen3.5-9B backbone and its System 1 decision head for llama.cpp.

A CPU-first BitNet student: Microsoft's BitNet b1.58 2B quantized to an I2_S GGUF with a float32 pointer head, distilled from Kev 9B logits and run through the bit-jev Python package with no separate open-weight licence.