OpenJev-Vision

Encode an image once. Answer several questions from a shared probability distribution.

OpenJev-Vision is an open research toolkit for visual probabilistic decisions. It includes original synthetic scenes, public-image experiments, downloadable trained readouts, and reproducible evaluations. The original OpenJev text-decision experiments remain available in this repository.

Vision guide · Vision weights · Dataset · Vision results · 中文

Start with vision

The release contains 8,192 original synthetic images, a 2,960-image Oxford-IIIT Pet subset, and 1,680 CLEVR-4 images. Synthetic scenes use a small CNN plus a supplied prior; public images use frozen DINOv2 features and trained readouts. These are separate experiments with fixed event vocabularies, not a general visual assistant. Queries use declared event semantics and a limited English parser.

git clone https://github.com/IamBusy/OpenJev-Vision.git
cd OpenJev-Vision
uv sync --frozen --extra vision --extra dev
uv run --no-sync openjev-vision download
uv run --no-sync openjev-vision predict \
  --checkpoint artifacts/openjev-vision-v0.1/synthetic-joint \
  --image examples/vision/scene-0.png \
  --prior examples/vision/scene-0-prior.json \
  --questions examples/vision/questions.json

Three-seed results include independent calibration and negative findings: the direct joint predictor degrades on new dependency structures, and independent attributes beat the tested joint and binding heads on unseen CLEVR-4 combinations. See the vision guide for public-image inference and reproduction.

Text-decision experiments

Hugging Face model · 中文 · Results · Reproduce · Model card

Small, local models for typed probabilistic decisions. The published model is OpenJev-0.6B; model revisions and software releases are tracked separately. Give OpenJev a state, questions and candidate descriptions. It returns probabilities for choice, noul (probability of truth), and ordinal score, without decoding answer tokens.

OpenJev is an independent research project inspired by TypeSafe's Jev. It is not affiliated with TypeSafe and does not reproduce Jev's undisclosed weights, architecture or RLCD algorithm. Training here uses supervised cross entropy and held-out temperature calibration. This is an experimental release.

What is included

v0.3 computes a state prefix once per request, then evaluates isolated question/candidate branches. A shared 1,024-parameter head scores candidates; 1,146,880 LoRA parameters are also trained. Question IDs and candidate keys are output handles, not model features. There is no persistent cross-request cache.

flowchart LR
  S[State] --> P[One prefix encoding]
  P --> A[Question 1 / candidate A]
  P --> B[Question 1 / candidate B]
  P --> C[Question 2 / candidates]
  A --> H[Shared scalar head]
  B --> H
  C --> H
  H --> N[Normalize within each question]

Quick start

Use Python 3.12 and uv. The measured runtime is Apple M3 Pro / macOS with BF16 inference. CPU uses FP32; CUDA acceleration is not implemented in this release. Allow several GB of disk space and RAM; the separately downloaded base weights are about 1.2 GB.

git clone https://github.com/IamBusy/OpenJev-Vision.git
cd OpenJev-Vision
uv sync --frozen --extra qwen --extra dev
uv run --no-sync openjev-model download
uv run --no-sync openjev-model predict --input examples/refund.json

download fetches a pinned Qwen base from Hugging Face and the small OpenJev adapter/head bundle from this repository's release. It checks the release archive and model file hashes. No model-service key is needed for inference or data reconstruction. Run commands from the checkout, or supply --root /path/to/OpenJev-Vision before the subcommand. Existing model files are verified rather than replaced.

from pathlib import Path
from openjev import OpenJevModel

root = Path.cwd()
model = OpenJevModel(root, checkpoint=root / "artifacts/openjev-0.6b")
result = model.predict(
    state="The recorded color is red.",
    questions={
        "color": {
            "type": "choice",
            "instructions": "Which candidate agrees with the recorded color?",
            "criteria": {"red": "The color is red.", "blue": "The color is blue."},
        }
    },
)
print(result["answers"]["color"])

The Python example uses uncalibrated probabilities. The CLI loads the delivered calibration file automatically. Calibration is specific to the measured mixture, not a guarantee for a new domain.

Load from Hugging Face

After installing the Qwen extra, no checkout-specific model directories are needed:

from openjev import OpenJevModel
model = OpenJevModel.from_pretrained("IamBusy/OpenJev-0.6B")

This loads the trained LoRA, custom scoring head, calibration and pinned base. See Hub usage and adapter versus merged weights.

Contract

A v0.3 request supports at most 512 total candidate branches, a 768-token state prefix and 192 tokens per candidate branch, including prompt overhead. Oversized inputs fail explicitly. Questions do not attend to each other. Independently scored candidates do not see the other candidates; set-relative instructions such as “none of the above” need separate validation.

Measured results and limits

The frozen experiment used 725 training judgments, 176 development judgments, 167 calibration judgments, 405 fresh test judgments and 448 older regression judgments. Whole state groups stay within splits. Public pretraining overlap cannot be ruled out.

DeepSeek answered 59/60 on the same small reference subset. v0.3 changes both architecture and data, so accuracy differences are not an architecture-only ablation. Some banking, access-policy and shipping tasks regress. A 255-candidate execution check establishes output shape, not general 255-way accuracy. Timings are in-process measurements on one M3 Pro, not production latency or a comparison with Jev. See all task results and regressions.

Local service

uv run --no-sync openjev-model serve --port 8081
curl http://127.0.0.1:8081/v1/decide \
  -H 'Content-Type: application/json' --data-binary @examples/refund.json

The service binds to localhost and serializes model calls. It has no authentication or production deployment controls. Its /v1/decide endpoint is an OpenJev contract, not a drop-in replacement for TypeSafe's API.

Development and reproduction

The vision quick start supports the data and CPU unit tests without PEFT. For development across all backends, install the full contributor environment:

uv sync --frozen --extra vision --extra dev --extra qwen
uv run --no-sync pytest -q
uv run --no-sync ruff check src tests scripts marketing
uv run --no-sync ruff format --check src tests scripts marketing
uv build

CI tests both vision + dev (without PEFT) and the full environment above. The tiny randomly initialized Qwen cache test, including forward and gradient equivalence, runs in both. Full model/data integration tests run when local artifacts are present and otherwise skip explicitly. Rebuilding the frozen data needs public source downloads but no provider credentials; see the complete reproduction guide.

• Names and versions, Contributing, security, changelog
• Data examples, source attribution
• v0.3 protocol, v0.2 report, v0.1 report

Code and the original scenario generator are Apache-2.0. Public dataset texts retain their source-specific licenses. Full corpora are fetched from pinned upstream sources; the repository includes a few attributed schema examples. Model weights and research limitations are documented in the model card. Credentials, raw provider traces, model caches and training runs are excluded from the source distribution.

Demos and publication source · Local storage and recovery