JEV-27B-VL-MLX-NVFP4
MLX conversion of autotrust/JEV-27B-VL for
mlx-vlm, with the System 1 LoRA (including its
lm_head update) merged and lm_head, the decision readout, kept unquantized. JEV is a decision model: it returns a
probability for every option of a question in one forward pass and does not
generate text.
JEV support is not yet in an mlx-vlm release:
pip install "git+https://github.com/Lazarus-931/mlx-vlm.git@feat/jev"
from mlx_vlm import load, predict
model, processor = load("nativ-community/JEV-27B-VL-MLX-NVFP4")
result = predict(model, processor, "The parcel arrived damaged and I want my money back.", {
"refund": {"type": "bool", "instructions": "Is the customer asking for a refund?"},
})
print(result["answers"]["refund"]["value"])
| Field | Value |
|---|---|
| Source | autotrust/JEV-27B-VL |
| Source revision | 4000d2393be6718e604f8e7dca563a780ab78e78 |
| Quantization | nvfp4, group size 16 |
| mlx-vlm | Lazarus-931/mlx-vlm@feat/jev |
| Verification | Same answer as autotrust's System 1 recipe (bf16 backbone + adapter_vllm merged with peft, option-token logits + bias, per-kind temperature) on 7/7 test questions (bool, choice, score, JSON state, 20 options in one pass, image, two images); identical token ids on 7/7 forward passes; largest probability gap 0.021 |