A listwise decision model with vision: give it a state (text, images, or both) and typed
questions — noul (is this statement true?), choice (pick one), score (an ordered scale) —
and it returns calibrated probabilities for every option, in a single forward pass, with no
text generation. It re-creates the Jev API's shape on an open base (Qwen3.5-4B), with images added.
This is the released checkpoint of the full vision run (step 600 of 1,200; chosen over the final step for its yes/no behaviour, see below). The earlier 300-step pilot stays at yah01/vjev-vision-pilot.
The web console and the HTTP API live in BubbleCal/vjev-serve:
pip install git+https://github.com/BubbleCal/vjev-serve
vjev-serve --model yah01/vjev-vision # then open http://localhost:8800
Or, with nothing but transformers, the single file in this repo:
from huggingface_hub import hf_hub_download
import importlib.util
spec = importlib.util.spec_from_file_location("vjev_infer", hf_hub_download("yah01/vjev-vision", "vjev_infer.py"))
vjev_infer = importlib.util.module_from_spec(spec); spec.loader.exec_module(vjev_infer)
m = vjev_infer.Vjev("yah01/vjev-vision") # cuda / mps / cpu
m.ask(state=["photo.jpg", "Frame from the warehouse camera, 12:40."],
questions={"person": {"type": "noul", "instructions": "There is a person in this image."},
"where": {"type": "choice", "instructions": "Where is the forklift?",
"criteria": {"left": "left half", "right": "right half", "none": "no forklift"}},
"busy": {"type": "score", "instructions": "How cluttered is the scene?",
"criteria": ["empty", "sparse", "busy", "crowded"]}})
Options inside one question compete (one softmax); questions never see each other. To rate
several independent things, ask one noul per thing (vjev-serve's multilabel type does this).
About 9 GB of memory in bf16/fp16.
| file | what |
|---|---|
model-*.safetensors, config.json, tokenizer and processor files | the full model, bf16: Qwen3.5-4B with the checkpoint's LoRA merged in |
head.pt | the listwise scoring head: one shared linear layer read at each option's slot |
vjev.json | how inputs are rendered (readout: trailing, pause: 0, length budgets) |
vjev_infer.py | stand-alone inference |
adapter/ | the same weights as a LoRA adapter (PEFT, r=32, α=64) over Qwen/Qwen3.5-4B, fitted on the nf4-quantized base |
The merged weights are the nf4-dequantized base the adapter was trained against, plus the adapter, in bf16 — no bitsandbytes needed. Against the adapter on its nf4 base (CUDA, 118 questions over 10 images): max |Δp| 0.0097, mean 0.0036, no answer changed.
Answer: (A) (B) …
slot, so options can see each other.| text stage (zero-shot) | pilot, 300 steps | this, step 600 | final step 1200 | |
|---|---|---|---|---|
| COCO geometry, choice accuracy | 0.506 | 0.665 | 0.735 | 0.703 |
| COCO geometry, choice ECE ↓ | 0.171 | 0.094 | 0.183 | 0.148 |
| VQAv2, choice accuracy | 0.579 | 0.654 | 0.706 | 0.721 |
| VQAv2, choice ECE ↓ | 0.126 | 0.033 | 0.052 | 0.070 |
| POPE adversarial, absent objects called present ↓ | 5.5% | 13.8% | 6.1% | 12.9% |
| POPE, AUC | 0.977 | 0.969 | 0.966 | 0.962 |
| text, 20k held-out rows: accuracy vs human labels | 0.796 | — | 0.799 | 0.797 |
| text, 20k held-out rows: ECE ↓ | 0.068 | — | 0.063 | 0.065 |
Step 600 was picked over the final step: the final step called absent objects present twice as often, for a small VQA gain. On text the vision stage cost nothing; the teacher itself scores 0.797 accuracy and 0.102 ECE on the same rows.
Options interact as in the teacher: adding a competing option protects the leader and takes its mass from the runners-up (a paired effect of +0.0066 over the order-permutation noise floor, CI excluding 0) — something a one-option-per-pass scorer cannot do.
Picture 1:, Picture 2:) rely on
the base model's ability.Apache-2.0. The base model, Qwen3.5-4B, is Apache-2.0.
A listwise decision model with vision: give it a state (text, images, or both) and typed
questions — noul (is this statement true?), choice (pick one), score (an ordered scale) —
and it returns calibrated probabilities for every option, in a single forward pass, with no
text generation. It re-creates the Jev API's shape on an open base (Qwen3.5-4B), with images added.
This is the released checkpoint of the full vision run (step 600 of 1,200; chosen over the final step for its yes/no behaviour, see below). The earlier 300-step pilot stays at yah01/vjev-vision-pilot.
The web console and the HTTP API live in BubbleCal/vjev-serve:
pip install git+https://github.com/BubbleCal/vjev-serve
vjev-serve --model yah01/vjev-vision # then open http://localhost:8800
Or, with nothing but transformers, the single file in this repo:
from huggingface_hub import hf_hub_download
import importlib.util
spec = importlib.util.spec_from_file_location("vjev_infer", hf_hub_download("yah01/vjev-vision", "vjev_infer.py"))
vjev_infer = importlib.util.module_from_spec(spec); spec.loader.exec_module(vjev_infer)
m = vjev_infer.Vjev("yah01/vjev-vision") # cuda / mps / cpu
m.ask(state=["photo.jpg", "Frame from the warehouse camera, 12:40."],
questions={"person": {"type": "noul", "instructions": "There is a person in this image."},
"where": {"type": "choice", "instructions": "Where is the forklift?",
"criteria": {"left": "left half", "right": "right half", "none": "no forklift"}},
"busy": {"type": "score", "instructions": "How cluttered is the scene?",
"criteria": ["empty", "sparse", "busy", "crowded"]}})
Options inside one question compete (one softmax); questions never see each other. To rate
several independent things, ask one noul per thing (vjev-serve's multilabel type does this).
About 9 GB of memory in bf16/fp16.
| file | what |
|---|---|
model-*.safetensors, config.json, tokenizer and processor files | the full model, bf16: Qwen3.5-4B with the checkpoint's LoRA merged in |
head.pt | the listwise scoring head: one shared linear layer read at each option's slot |
vjev.json | how inputs are rendered (readout: trailing, pause: 0, length budgets) |
vjev_infer.py | stand-alone inference |
adapter/ | the same weights as a LoRA adapter (PEFT, r=32, α=64) over Qwen/Qwen3.5-4B, fitted on the nf4-quantized base |
The merged weights are the nf4-dequantized base the adapter was trained against, plus the adapter, in bf16 — no bitsandbytes needed. Against the adapter on its nf4 base (CUDA, 118 questions over 10 images): max |Δp| 0.0097, mean 0.0036, no answer changed.
Answer: (A) (B) …
slot, so options can see each other.| text stage (zero-shot) | pilot, 300 steps | this, step 600 | final step 1200 | |
|---|---|---|---|---|
| COCO geometry, choice accuracy | 0.506 | 0.665 | 0.735 | 0.703 |
| COCO geometry, choice ECE ↓ | 0.171 | 0.094 | 0.183 | 0.148 |
| VQAv2, choice accuracy | 0.579 | 0.654 | 0.706 | 0.721 |
| VQAv2, choice ECE ↓ | 0.126 | 0.033 | 0.052 | 0.070 |
| POPE adversarial, absent objects called present ↓ | 5.5% | 13.8% | 6.1% | 12.9% |
| POPE, AUC | 0.977 | 0.969 | 0.966 | 0.962 |
| text, 20k held-out rows: accuracy vs human labels | 0.796 | — | 0.799 | 0.797 |
| text, 20k held-out rows: ECE ↓ | 0.068 | — | 0.063 | 0.065 |
Step 600 was picked over the final step: the final step called absent objects present twice as often, for a small VQA gain. On text the vision stage cost nothing; the teacher itself scores 0.797 accuracy and 0.102 ECE on the same rows.
Options interact as in the teacher: adding a competing option protects the leader and takes its mass from the runners-up (a paired effect of +0.0066 over the order-permutation noise floor, CI excluding 0) — something a one-option-per-pass scorer cannot do.
Picture 1:, Picture 2:) rely on
the base model's ability.Apache-2.0. The base model, Qwen3.5-4B, is Apache-2.0.