Jev-Style

Name: Jev-Style
Author: lawrence3699

by lawrence3699version 0.6.3i★ –iRuns on your computer

Registry nameio.github.lawrence3699/jev-style

Install

Overview

Description from the maker (original English)

Local 2B and 0.8B decision models: calibrated yes/no, choice and score answers for your agent.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: JEV_STYLE_URL

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "jev-style": {
      "command": "uvx",
      "args": [
        "jev-style",
        "mcp"
      ],
      "env": {
        "JEV_STYLE_URL": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add jev-style --env JEV_STYLE_URL=your-value -- uvx jev-style mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "jev-style": {
      "command": "uvx",
      "args": [
        "jev-style",
        "mcp"
      ],
      "env": {
        "JEV_STYLE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "jev-style": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "jev-style",
        "mcp"
      ],
      "env": {
        "JEV_STYLE_URL": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

JEV_STYLE_URL env
URL of a running `jev-style serve` (start it with `pip install "jev-style[torch]"` or `[mlx]` on Apple silicon, then `jev-style serve`).

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account lawrence3699, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32715 servers in this category
Published to the registry more recently than 86% of the other 32715 servers in this category
Basic checks equal to or better than 100% of the other 32715 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Jev-Style safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.6.3) reached the registry 5 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, First Sale Scout

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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.

Release (`--release`)	Parameters · smallest build	JevBench v1.4.1 public (231)	tweet_topic, zero-shot (1,693)	Context
Jev-Style-2B-Decision-v4 (`2b`)	2.2B · 1.27 GB (Q4_K_M, text)	74.9 %	not run	32,768 tokens, images
Jev-Style-2B-Decision-v3 (`2b-v3`)	1.9B · 1.27 GB (Q4_K_M)	73.6 %	82.2 %	25,600 tokens
Jev-Style-0.8B-Decision-v3 (`0.8b`, default)	0.8B · 0.53 GB (Q4_K_M)	64.1 %	75.5 %	25,600 tokens
Hosted Jev 1.13, for reference	–	86.6 %	79.3 %	–

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

Model	Banking77 (77 intents, never trained)	MASSIVE intent, 37 held-out languages	tweet_topic, zero-shot	JevBench v1.4.1 public (231)
Jev-Style-0.8B-Decision-v3	68.2 %	65.5 %	75.5 %	64.1 %
Best official Laya checkpoint (0.8B, 1,024 tokens by default)	49.2 %	36.1 %	63.2 %	58.4 %

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

Build	2B v4	2B v3	0.8B	Used by
safetensors: 2B v4, 2B v3, 0.8B	4.43 GB	3.76 GB	1.50 GB	`--backend torch` (CUDA, Apple MPS, CPU); the 2B v4 reads images here
MLX bf16 / 8-bit: 2B v4, 2B v3, 0.8B	3.76 / 2.00 GB	3.76 / 2.00 GB	1.50 / 0.80 GB	`--backend mlx` (Apple silicon; `auto` picks it there)
GGUF F16 / Q8_0 / Q4_K_M: 2B v4, 2B v3, 0.8B	3.78 / 2.01 / 1.27 GB	3.78 / 2.01 / 1.27 GB	1.52 / 0.81 / 0.53 GB	`--backend gguf` (llama.cpp through the release's scorer: `jev-score-v2` for both 2Bs, `jev-score` for the 0.8B)

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

Only the beginning is shown; read the rest in the repo

Jev-Style

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

First Sale Scout

README from the maker

Quick start

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

Why Jev-Style

Models