Jev-Style

Jev-Style

Small decision models that run on your own machine. Text and images in, a calibrated probability for every option out.

Website · Models · Live demo · PyPI · Agent skills · MCP · Changelog · 简体中文

Created and maintained by Chaoliang Yan . An independent project, not affiliated with TypeSafe or Jev ( details ).

Jev-Style is a family of small decision models (0.8B and 2B, fine-tuned from Qwen3.5) and the toolkit that puts them to work. Give a model text or JSON, optional images and typed questions. It reads the input once and returns a calibrated probability for every option, in one forward pass, on your laptop. Use it to route support tickets to the right team, pick the tool or model an agent should call, tag topics, flag phishing, check receipts and screenshots, or let a coding agent's tool calls through only when a local model agrees. No GPU, no API key and no training needed.

Quick start

Try it first with nothing installed: the 2B Space runs Jev-Style-2B-Decision-v4 (text and images) and the 0.8B Space runs the 0.8B.

Locally, you need Python 3.10 or newer. In a virtual environment, install one backend for your machine:

pip install -U "jev-style[mlx]"      # Apple silicon (text)

pip install -U "jev-style[torch]" # NVIDIA GPU, CPU or image input; Intel Macs, Linux and Windows (also works on Apple MPS)

The bare pip install jev-style installs only the HTTP client, the fake engine and the MCP server; running a model needs one of the extras above. [mlx] pins mlx-lm to 0.31.3 on purpose (the 2B MLX runtimes check the mlx-lm source they patch and refuse any other version), so give jev-style its own virtual environment, or use uv tool install, if other MLX tools in the same environment need a newer mlx-lm.

Start the server and open the local Playground. The first start downloads the 0.8B weights (about 1.5 GB); later starts reuse them.

jev-style serve                      # add --release 2b for the 2B v4 (images need --backend torch)

In a second terminal, make one decision:

jev-style decide "I was charged twice" --url http://127.0.0.1:8765 --choice "Which team?::billing,shipping,returns"

Or from Python, in one line (loads the model in-process, or uses the server in JEV_STYLE_URL):

import jev_style

jev_style.classify("Where is my parcel? It was due Monday.", ["billing", "shipping", "tech"])

{'label': 'shipping', 'confidence': ..., 'probabilities': {'billing': ..., 'shipping': ..., 'tech': ...}}

The server speaks the public POST /v1/systemone request shape, so clients written for Jev-compatible servers, including the official SDKs, can point at http://127.0.0.1:8765 with any API key string. For the command line tool on its own: uv tool install "jev-style[all]" on Apple silicon, uv tool install "jev-style[torch]" elsewhere.

Why Jev-Style

• Three question types in one request. Yes/no (noul), multiple choice (choice, up to 255 options) and ordered ratings (score, 2 to 10 levels). The model reads the text once and answers every question about it.
• Probabilities, not just labels. Each release ships temperatures fitted on held-out data, so your code can act on confident answers and send the rest to a person or an LLM.
• Images (2B v4). Photos, screenshots, receipts, charts and scanned forms in the state, sent as assets the way TypeSafe's API takes them. The PyTorch build reads them; the MLX and GGUF builds read text only.
• Long inputs. Up to 32,768 tokens per call with the 2B v4 (25,600 with v3). Nothing is truncated: an input that is too long is rejected with an error that says so.
• Runs locally, small. 0.53 GB for the 0.8B at 4-bit; about 0.15 to 0.2 s for a short request with MLX on an M1 Max, after the first call. Nothing leaves your machine.
• 51 languages evaluated. Training covers 19 languages. On MASSIVE intent the 0.8B beats Laya's multilingual checkpoint in all 51 evaluated languages.
• Built for agents. Six agent skills, an MCP server in the official registry, a Claude Code tool-call guard and confidence cascades, all working with Claude Code, Codex, Cursor and other agents.
• Measured, with the protocol published. jev-style eval scores your own labelled data; every benchmark number here links to a model card with its protocol and confidence intervals.

Models

Start with the default 0.8B to try things: 1.5 GB, fast on a laptop. Move to the 2B v4 for accuracy, images and 32,768-token inputs. --release 2b loads the v4; --release 2b-v3 keeps the previous 2B.

Decision Index. Since 2026-11-04 the 2B v4 is listed on the public Decision Index 0.3 board as jev-style-2b-v4, replacing the 2B v3 entry (34.05): Public 44.37, Full 34.53, Breadth 41.75, and on the Vision board Public 57.55 / Full 51.94. The 0.8B v3 is on the same board at Public 17.0 / Full 12.5. The Full score also counts the maintainers' private test sets, which is why it is lower than the Public score.

How to read the JevBench numbers. The 2B v4 JevBench score is one self-run with the official harness through this package's server (float32, Apple MPS; 95 % CI 68.9 to 80.0 %), not a JevBench board entry. Several benchmarks' official train splits are in the 2B v4's training data, so those results are not zero-shot. The 2B v3 numbers are single pre-declared runs with its GGUF F16 build: on JevBench it is the highest among the Qwen3.5-2B-family systems on the v1.4.1 board (decider-2b 71.0 %, open-jev-zefan-2b 64.5 %), but the lead over decider-2b is inside the 95 % confidence interval, 42 of the 82 board systems score higher, and hosted Jev is well ahead. Each model card gives every protocol: 2B v4, 2B v3, 0.8B v3.

The 0.8B against Laya, on sets neither was trained on

From the 0.8B model card, which gives the full protocol and confidence intervals. The Laya rows are its official checkpoints re-run on the same rows, except tweet_topic and JevBench, which use published numbers. The hosted Jev API has higher accuracy than the 0.8B on every one of these sets where its accuracy is published. On tweet_topic the 2B v3's accuracy is above Jev's published number, but its macro-F1 is below (0.678 vs 0.694).

Every build and its size

These are weight-file sizes, not RAM or VRAM requirements. Each build carries its own runtime file next to the weights; the server downloads a pinned revision a