SimpleJev / JevAny

🇺🇸 English | 🇨🇳 简体中文 
 🎮 Results & Demos |
 ⚡ Quickstart |
 💻 Run locally |
 🤗 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 
Interactive benchmark results.
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 run a model locally to
try your own inputs and see its choices and probabilities.
### ⚡ Jev inside LLM agent loops
The LLM plans, recovers and finishes the task; Jev picks the next action
wherever the loop offers a bounded set of candidates. Each animation replays
one recorded pair, LLM only on the left and LLM + Jev on the right, with the
measured totals on the clocks and schematic step artwork.
**1. WebShop.**
Jev picks the requested color and size from menus the LLM generated, and the
LLM buys the product.
**2. FrozenLake.**
One LLM plan, then Jev compares the four moves at each new state.
**3. Terminal-Bench.**
On `sqlite-db-truncate`, Jev picks raw-page inspection out of three probes, and
the LLM recovers and verifies ten rows.
Overview · All demos
Full results,
delegation protocol
and technical report.
## 📑 Table of Contents
- 🎮 Results and Demos
- ⚡ 1. Quickstart
 - 💻 1.1 Run locally
 - 🛠️ 1.2 JevAny Training
 - 🚀 1.3 JevAny Deployment
- 🤗 2. Pretrained Models
 - Training-free choice-token readout
- 📊 3. Benchmark Results
 - ⏱️ 3.1 Inference efficiency
- 🕹️ 4. Examples & Test Environments
- 🧩 5. Supported Model Families
- 📚 6. Documentation and Contributing
 - 🤝 Contributing
## ⚡ 1. Quickstart 
Use Python 3.12 or newer. Clone the repository and install the lightweight package:
 [code] 
Keep this environment active and work from the repository root. Start with a
local demo, then train on your own data or use the API.
### 💻 1.1 Run locally 
Choose a model that fits your computer:
| Model | Hardware | Start here |
|---|---|---|
| Qwen 0.8B starter | CPU · 16 GB RAM recommended | Train the small adapter on the bundled tickets |
| JevAny-Qwen 4B | CUDA · ~8 GB for BF16 base weights, plus runtime memory | Load the released model |
| JevAny-Qwen 27B | CUDA · ~54 GB for BF16 base weights, plus runtime memory | Choose the larger checkpoint |
The local model guide covers preparation and loading.
Released models download on first use and reuse the local cache. With the model
server running, open a second terminal in the same checkout:
 [code] 
Open `http://127.0.0.1:8090`, choose **Test and connect**, then edit
**Try your own decision** and press **Ask the model**. Change the state or options
to see how its decision changes. Games, robotics and replays
are avai