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🎮 Results & Demos | ⚡ Quickstart | 🤗 Models | 📊 Benchmarks | 📚 Docs

JevAny is open infra for System 1 decision model training and deployment, covering data preparation, model adaptation and evaluation. Use a released model or train on your own data to route support tickets, select tools, or choose a robot's next action. One API takes the state, question and candidate options, then directly returns a choice and its probabilities.

🎮 Results and Demos

Full benchmark results and evaluation details.

The following 30 examples are archived replays from an earlier compatible JevAny checkpoint. The current default release is JevAny-Qwen3.8-27B. Explore the cases, or try the playground to inspect recorded actions and option probabilities.

📑 Table of Contents

• 🎮 Results and Demos
• ⚡ 1. Quickstart
  • 🛠️ 1.1 JevAny Training
  • 🚀 1.2 JevAny Deployment
• 🤗 2. Pretrained Models
• 📊 3. Benchmark Results
• 🕹️ 4. Examples & Test Environments
• 🧩 5. Supported Model Families
• 📚 6. Documentation and Contributing

⚡ 1. Quickstart

Use Python 3.12 or newer. Clone the repository and install the lightweight package:

git clone https://github.com/SimpleJev/JevAny.git
cd JevAny
python3.12 -m venv .venv
source .venv/bin/activate
python -m pip install -e .

Keep this environment active and work from the repository root. Choose Training to build your own model or Deployment to use a released checkpoint. For a preview on CPU, try the playground.

🛠️ 1.1 JevAny Training

Train your own System 1 model on the same state and questions you send at inference, with a label for each question. Start with the bundled synthetic support tickets, then train on your own labelled data. The starter recipe uses Qwen3.5-0.8B on CUDA with BF16 and writes runs/my-jev:

python -m pip install -e '.[train]'
jevany data init --out data/starter
jevany data validate data/starter/train.jsonl
jevany train --config recipes/sft.toml --dry-run
jevany train --config recipes/sft.toml

After training, try the checkpoint on the included ticket request:

jevany decide examples/request.json --checkpoint runs/my-jev

Pass --data to train on your own JSONL data, or use recipes/finetune.toml to adapt the released 27B model. See the training guide for CPU settings, multimodal data and standard torchrun launches. For image/video training or fine-tuning the released 27B model, install .[train,multimodal].

After SFT, you can continue with experimental RLCR, which rewards correctness and probability calibration:

jevany train --config recipes/rlcr.toml

🚀 1.2 JevAny Deployment

Install the serving dependencies and start the released Qwen 4B model on a CUDA GPU. See the hardware and loading guide for memory requirements.

python -m pip install -e '.[serve,multimodal]'
jevany serve --checkpoint SimpleJev/JevAny-Qwen3.5-4B-LoRA \
  --device cuda --dtype bf16 --port 8008

The default path favors reproducibility. CUDA deployments can opt into BF16 LoRA merging, SDPA and torch.compile; the useful settings differ between 4B and 27B. See the inference acceleration guide for commands, H200 measurements and accuracy caveats.

To serve your training output, replace the checkpoint ID with runs/my-jev. Keep the server running. In a Python session using the same environment, send a ticket and the departments that can handle it:

from jevany import Choice, JevClient

jev = JevClient("http://127.0.0.1:8008")
result = jev.system_one(
    state={"ticket": "I was charged twice. Please help."},
    questions={
        "department": Choice(
            instructions="Which team should handle this?",
            criteria={"billing": "Payment problems", "shipping": "Delivery problems"},
        ),
    },
)
answer = result["answers"]["department"]
print("Selected team:", answer["choice"])
print("Probabilities:", answer["probabilities"])

choice is one of the department names; probabilities maps each name to its probability. Your application can use these fields to route the ticket or ask for review when the decision is uncertain. Use Noul for yes/no questions, such as whether a ticket needs urgent review, and Score for ordered levels, such as low, normal and high priority. See the API reference for all three question types.

For in-process inference, load a model in Python and use the same interface. For image and video inputs, follow the media setup.

🤗 2. Pretrained Models

Model	Readout	Intended use
JevAny-Gemma-4B	Pointer	Compact Gemma release
JevAny-Qwen3.5-4B	Pointer	Compact, flexible choice count
JevAny-Qwen3.5-4B-Direct-Token	Direct-token	Best released 4B JevBench accuracy
JevAny-Qwen3.8-27B	Pointer	Default; highest released accuracy
JevAny-Muse-Glimmer-30B	Pointer	Muse Glimmer alternative

These LoRA adapters were trained with SFT on 1,772,725 text records containing 2,180,242 labelled decisions; see training compute and experiments for the setup. Full-parameter SFT and further post-training improvements are planned.

The corresponding base model is loaded separately and its license and access terms apply. Allow roughly twice the base parameter count in bytes for BF16 weights, plus runtime memory. See the hardware and loading guide.

Pointer and direct-token models share the same API. Pointer supports up to 4,096 options within the context limit; direct-token supports up to 255. See readout choices for training and accuracy tradeoffs.

📊 3. Benchmark Results

JevAny-Qwen3.8-27B leads both benchmarks and has the lowest NLL and Brier. Among 4B releases, direct-token leads on JevBench; pointer leads on Transfer.

Model	Transfer ↑	JevBench ↑	NLL ↓	Brier ↓	ECE ↓
Kev-4B	74.19%	75.32%	0.858	0.380	0.125
Kev-27B	82.31%	85.28%	0.533	0.265	0.050
Jev 1.13.0	85.37%	86.58%	0.644	0.212	0.033
Laya	52.29%	58.01%	1.264	0.615	0.127
JevAny releases
JevAny-Gemma-4B	70.84%	77.49%	0.706	0.369	0.056
JevAny-Qwen3.5-4B	78.68%	80.09%	0.587	0.297	0.035
JevAny-Qwen3.5-4B-Direct-Token	78.20%	80.95%	0.564	0.291	0.029
JevAny-Muse-Glimmer-30B	83.46%	87.45%	0.464	0.229	0.032
JevAny-Qwen3.8-27B	86.04%	90.04%	0.388	0.195	0.026

NLL, Brier and ECE are measured on Transfer.

Full results and protocols · Machine-readable results · Method and ablation report

🕹️ 4. Examples & Test Environments

The playground includes the three environments below. These GIFs preserve historical model actions and option probabilities; run the current JevAny-Qwen3.8-27B checkpoint with the commands in the playground guide.

🤖 4.1 Robot peg insertion

Use a Franka gripper to grasp, align and insert a peg, checked by PyBullet contact physics.

🔫 4.2 Doom corridor · 3D

Clear the final room by defeating the enemies on the left and right, then move forward. The environment uses ViZDoom and the included Freedoom assets.

⛏️ 4.3 Crafter survival · 2D

Gather wood, craft tools and mine stone while managing health and supplies.

🎮 4.4 Try the playground

From the Quickstart environment, start the playground:

jevany demo

Open http://127.0.0.1:8090 and choose Replay to watch a recorded run. The bundled recordings play locally on CPU.

To run your model, keep the server from Deployment running. Stop the replay viewer with Ctrl+C, install the optional game engines, then restart the playground with the server address:

python -m pip install -e '.[demo]'
jevany demo --base-url http://127.0.0.1:8008 --text-only

Choose Run model in the browser, or Play yourself to control the game. Live control sends text state to the model. Robot control uses the .[robotics] extra. See the playground guide for platform requirements and environment APIs, or integrations to combine JevAny decisions with an LLM planner.

🧩 5. Supported Model Families

Model IDs, supported inputs and setup requirements.

📚 6. Documentation and Contributing

Training · Deployment · API · Data · Evaluation · Contributing

To contribute a model adapter, evaluation or application example, start with the contribution guide. The technical report describes the model design, multimodal path, experiments and open questions.

Code and starter data are Apache-2.0. Some components are adapted from Kev; see NOTICE and ACKNOWLEDGEMENTS.md. Base models and upstream datasets retain their own terms.