---
license: mit
pretty_name: MoJev
base_model: Qwen/Qwen3.5-0.8B
datasets:
  - MoLeMo-Lab/mojev-mix
library_name: transformers
pipeline_tag: text-classification
tags:
  - calibration
  - structured-output
  - multiple-choice
  - preference-learning
  - multimodal
  - mojev
model-index:
  - name: MoJev
    results:
      - task:
          type: text-classification
          name: Typed decision scoring
        dataset:
          type: MoLeMo-Lab/mojev-mix
          name: MoJev-Mix test
          split: test
        metrics:
          - type: accuracy
            value: 0.9323
            name: Accuracy
---

<img align="right" src="https://molemo-lab.github.io/mojev/assets/molemo-logo.png" width="76" alt="MoLeMo Lab logo">

# MoJev

[![Homepage](https://img.shields.io/badge/Project-Homepage-AD1853)](https://molemo-lab.github.io/mojev/)
[![Hugging Face Space](https://img.shields.io/badge/Try-Space-AD1853?logo=huggingface)](https://huggingface.co/spaces/di-zhang-fdu/mojev)
[![Preprint](https://img.shields.io/badge/Preprint-PDF-AD1853)](https://github.com/MoLeMo-Lab/mojev/blob/master/paper/mojev-preprint.pdf)
[![Code](https://img.shields.io/badge/GitHub-Code-24292F?logo=github&logoColor=white)](https://github.com/MoLeMo-Lab/mojev)
[![Dataset](https://img.shields.io/badge/Hugging_Face-Dataset-FFD21E?logo=huggingface&logoColor=111111)](https://huggingface.co/datasets/MoLeMo-Lab/mojev-mix)

**Typed, calibrated decisions in one forward pass.**

Contact: [contact@molemo.org](mailto:contact@molemo.org)

This is the trained checkpoint for the
[`mojev`](https://github.com/MoLeMo-Lab/mojev) runtime. It scores
request-time candidate values from unstructured state and returns schema-bound
probability distributions.

| MoJev family resource | location |
|---|---|
| Code | [MoLeMo-Lab/mojev](https://github.com/MoLeMo-Lab/mojev) |
| Model | **MoLeMo-Lab/mojev** |
| Dataset | [MoLeMo-Lab/mojev-mix](https://huggingface.co/datasets/MoLeMo-Lab/mojev-mix) |
| Results | [MoJev results](https://github.com/MoLeMo-Lab/mojev#results) |
| Preprint | [MoJev (PDF)](https://github.com/MoLeMo-Lab/mojev/blob/master/paper/mojev-preprint.pdf) |
| Project page | [MoJev](https://molemo-lab.github.io/mojev/) |

## Interactive demo

[Try MoJev on Hugging Face Spaces](https://huggingface.co/spaces/di-zhang-fdu/mojev):
text, one or multiple images, a question, and custom candidates are scored on
server-side ZeroGPU. No model weights are downloaded to the browser.

The optional [`browser/`](browser) text export uses asymmetric INT4 linear weights,
INT8 token embeddings, and an FP32 decision head. Export scripts and numerical comparisons are described in
the [browser guide](https://github.com/MoLeMo-Lab/mojev/tree/master/browser).

## Model contract

| input | released configuration |
|---|---|
| state | text and local image references; 16,384-token training truncation |
| question | instruction text |
| candidates | request-time strings |
| output | logits decoded as `Choice`, `Noul`, or `Score` distributions |

Candidate names are supplied by the caller and encoded directly from their text.

MoJev's Qwen3.5 backbone supports 262,144 tokens natively and up to
1,010,000 tokens with [YaRN scaling](https://github.com/vllm-project/recipes/blob/main/Qwen/Qwen3.5.md).
The checkpoint records the 16,384-token training window. The MoJev runtime
accepts a larger inference state window through `--context-tokens`; the packed
sequence also includes question and candidate tokens.

## Run with the MoJev server

```sh
git clone https://github.com/MoLeMo-Lab/mojev
cd mojev
pip install -e '.[transformers]'

mojev serve MoLeMo-Lab/mojev --port 8000
```

For a longer state within the native context:

```sh
mojev serve MoLeMo-Lab/mojev --port 8000 --context-tokens 65536
```

```python
from typesafe_sdk import Choice, TypeSafeClient

with TypeSafeClient(api_key="local", base_url="http://127.0.0.1:8000") as client:
    result = client.system_one(
        state={"document": "I was charged twice. Please fix this ASAP."},
        questions={
            "category": Choice(
                instructions="What is this ticket about?",
                criteria={"billing": None, "technical": None, "other": None},
            )
        },
    )

print(result.choices["category"].choice)
print(result.choices["category"].probabilities)
```

## Load with Transformers

```python
from transformers import AutoModel, AutoProcessor

model = AutoModel.from_pretrained(
    "MoLeMo-Lab/mojev",
    trust_remote_code=True,
).to("cuda").eval()
processor = AutoProcessor.from_pretrained(
    "MoLeMo-Lab/mojev",
    trust_remote_code=True,
)
```

The model directory contains the scorer implementation through `auto_map`.
Packing, candidate sorting, and schema decoding are provided by the
[`mojev`](https://github.com/MoLeMo-Lab/mojev) package.

## Multimodal input

```sh
pip install -e '.[transformers]'
mojev serve MoLeMo-Lab/mojev --port 8000
```

Use the image marker followed by an absolute path in the request state:

```python
from pathlib import Path

image = Path("examples/cat.jpg").resolve()
state = f"Identify the subject. <|vision_start|><|image_pad|><|vision_end|>{image}"
```

The processor expands the image into visual patch tokens in the state branch.
Every question and candidate in the request can attend to those tokens.

| candidate set | grey image P(cat) | cat image P(cat) |
|---|---:|---:|
| `cat`, `dog` | 0.471 | **0.786** |
| `cat`, `dog`, `car`, `other` | 0.264 | **0.528** |

## Evaluation

On 12,000 evaluation decisions, the released checkpoint reaches **93.23%**
accuracy with **0.79%** expected calibration error.

## Architecture

![MoJev architecture: TreePacked Attention and typed decision head](https://molemo-lab.github.io/mojev/assets/model-architecture.svg)

- Base: Qwen3.5-0.8B; all 854,036,544 parameters trained.
- Packing: state, questions, and candidates in one sequence.
- Attention: tree mask with isolated question/candidate branches.
- Readout: rank-512 context and candidate projections with scaled dot product.
- Objective: Plackett–Luce ranking plus Brier calibration loss.
- Precision: bf16 encoder and fp32 readout.

## Training

| item | value |
|---|---|
| data | 205,084 rows from 18 Open-Jev generators |
| epochs | 1 |
| parallelism | 8-way data parallel |
| learning rate | 1e-5 |
| state truncation | 16,384 tokens |
| Brier weight | 1.0 |
| wall time | 47 minutes |

## Applications

- routing and triage;
- policy and evidence classification;
- tool and workflow selection;
- calibrated execution, deferral, and escalation thresholds;
- multiple typed decisions over shared state.

Code is MIT licensed. The Qwen base model license applies to the checkpoint.