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jev-style-v3

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jev-style-v3 / README.md

chaoliangUNSW

Install line names the backend extra: pip install "jev-style[torch]" ([mlx] on Apple silicon)

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metadata

title: Jev-Style v3
emoji: ⚖️
colorFrom: gray
colorTo: blue
sdk: gradio
sdk_version: 6.28.0
python_version: '3.12'
app_file: app.py
pinned: true
license: apache-2.0
short_description: 0.8B decisions, a probability for every option
models:
  - chaoliangUNSW/Jev-Style-0.8B-Decision-v3
  - chaoliangUNSW/Jev-Style-0.8B-Decision-v3-GGUF
  - chaoliangUNSW/Jev-Style-0.8B-Decision-v3-MLX
preload_from_hub:
  - >-
    chaoliangUNSW/Jev-Style-0.8B-Decision-v3
    LICENSE,NOTICE,chat_template.jinja,config.json,generation_config.json,jev_style_decision.py,manifest.json,model.safetensors,readout_config.json,release_config.json,requirements.txt,tokenizer.json,tokenizer_config.json
    4635f7eb619ac1683fe9776ec436518f070eb20d
tags:
  - text-classification
  - llm-routing
  - guardrails
  - calibration

Jev-Style v3

Try Jev-Style-0.8B-Decision-v3: give it a text and a question, get a calibrated probability for every option. Choice, yes/no or score; up to 25,600 tokens of input.

Runs the model repo's own PyTorch runtime (float32) on ZeroGPU. Other builds: GGUF (0.53 GB in 4-bit) · MLX. Website: jevstyle.com.

Run it on your own machine: pip install "jev-style[torch]" ([mlx] on Apple silicon; PyPI), then jev-style serve for a local API, Playground, agent skills, Claude Code guard and MCP tools: github.com/lawrence3699/jev-style.

The 19K-token example is public-domain text (U.S. founding documents, Project Gutenberg eBooks 1, 5, 2 and 1404). Code: Apache-2.0. Not affiliated with TypeSafe, Jev or Laya.