siliconflow/kev

siliconflow/kev

Description: tiny Jev-like model built on top of Qwen2.5-0.5B you can train and run on your MacBook

Language: Python

License: Apache-2.0

Stars: 0

Forks: 0

Open issues: 0

Created: 2026-09-20T07:37:07Z

Pushed: 2026-09-21T05:55:02Z

Default branch: main

Fork: yes

Parent repository: jaredpalmer/kev

Archived: no

README:

Kev

Small Jev-like decision models you can train and run yourself.

Kev is a family of small decision models built on Qwen3.5 and based on the architecture described in Jev's Architecture Unmasked. You can use the pretrained weights or train your own. The API matches TypeSafe's System One, so you can point their Python SDK at your local server.

Highlights

• 0.8B, 4B, and 9B models, with training code and evaluation data.
• Yes/no (noul), multiple-choice (choice), and rating (score) questions in the same request.
• Questions share the input text but can't read each other.
• Runs on CUDA and Apple Silicon. The 4B and 9B models fit a 32 GB Mac using bf16; see [Serving Performance](#serving-performance) for what to expect on a Mac.
• A web playground for trying your own inputs and checking how option order affects the answers.


Quick Start

You'll need Python 3.12+ and uv.

git clone https://github.com/jaredpalmer/kev.git && cd kev
uv sync --extra serve
KEV_DTYPE=bf16 uv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b --port 8009

This starts Kev-4B locally. The first run downloads the adapter and base model. --run also accepts a local checkpoint directory or a Hub revision, such as jaredpalmer/kev-4b@qwen3 for the previous generation.

In another terminal, send it a ticket:

curl -s localhost:8009/v1/systemone -H 'content-type: application/json' -d '{
"state": "Shoes arrived two weeks late and in the wrong size. Also I see two charges on my card.",
"model": "kev-latest",
"questions": {
"department": {"type": "choice", "instructions": "Which team should handle this?",
"criteria": {"returns": "Exchanges, refunds, wrong or damaged items",
"shipping": "Delivery status, delays, lost packages",
"billing": "Charges, invoices, payment problems"}},
"escalate": {"type": "noul", "instructions": "Does this need urgent human attention?"},
"frustration": {"type": "score", "instructions": "How frustrated is the customer?",
"criteria": ["Calm", "Frustrated", "Very angry"]}
}}'

Example response from Kev-4B, running in bf16 on an Apple M5:

{
"model": "kev-latest",
"answers": {
"department": { "type": "choice", "choice": "returns", "confidence": 0.21,
"probabilities": { "returns": 0.47, "shipping": 0.28, "billing": 0.25 } },
"escalate": { "type": "noul", "noul": 0.93 },
"frustration": { "type": "score", "score": 1.44, "confidence": 0.78,
"legend": { "0": "Calm", "1": "Frustrated", "2": "Very angry" },
"probabilities": { "0": 0.00, "1": 0.56, "2": 0.44 } }
},
"usage": { "input_tokens": 101, "output_tokens": 161 },
"latency_ms": 495
}

The ticket mentions a return, a late delivery, and a billing problem, and the department probabilities say so. That is the point of getting probabilities back instead of a single label.

Python

The TypeSafe SDK is included in uv sync --extra serve:

from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

client = TypeSafeClient(
api_key="local",
base_url="http://127.0.0.1:8009",
model="kev-latest",
)
response = client.system_one(
state="I was charged twice. Please fix this ASAP.",
questions={
"billing": Noul(instructions="Is this ticket about billing?"),
"tone": Choice(
instructions="What is the customer's tone?",
criteria={"calm": None, "frustrated": None, "angry": None},
),
"urgency": Score(
instructions="How urgent is this ticket?",
criteria=["can wait", "this week", "today"],
),
},
)
print(response.nouls["billing"].noul)
print(response.choices["tone"].choice)
print(response.scores["urgency"].score)

Playground

With the server still running, open another terminal. You'll need Node 20.9+:

cd playground
npm install
npm run dev -- -p 3001

Open localhost:3001, load a preset, and edit the text and questions. Press ⌘↵ to run it. "Packed vs separate" compares asking all questions at once with asking them one at a time. "Permute" runs a Choice question with six option orders. There are also presets for testing question isolation and fake delimiter tokens.

There's a chess demo, too. The board is the input, legal moves are Choice options, and a Score question rates the position. You can play against Kev or let it play itself. Games are saved in localStorage.


Models

Start with Kev-4B. Use Kev-9B when accuracy and calibration matter more than memory. Use Kev-0.8B if you need the smallest model. All three are built on Qwen3.5 bases with the same training data and settings.

| Model | Base | Accuracy: Trained Sources | Accuracy: New Sources | Brier: New Sources | Model Card | |---|---|---|---|---|---| | Kev-0.8B | Qwen3.5-0.8B-Base | 0.829 / 0.827 | 0.643 / 0.668 | 0.513 / 0.473 | [Details](docs/model-cards/kev-0.8b.md) | | Kev-4B | Qwen3.5-4B-Base | 0.877 / 0.870 | 0.794 / 0.832 | 0.316 / 0.266 | [Details](docs/model-cards/kev-4b.md) | | Kev-9B | Qwen3.5-9B-Base | 0.876 / 0.873 | 0.812 / 0.837 | 0.291 / 0.243 | [Details](docs/model-cards/kev-9b.md) | | Jev | Hosted | 0.845 / – | 0.857 / – | 0.211 / – | – |

Each cell is development / test. "Trained sources" means held-out examples from the datasets used to train Kev. "New sources" means datasets and policy rule types Kev wasn't trained on. Every model was evaluated on the same development sets (decision-v7, transfer-v4) and the same test sets, which were read once per released checkpoint, after model selection. Lower Brier is better.

Kev-9B trails Jev by about 4.5 points on the new-source development set. We don't know which datasets Jev was trained on, so this isn't a controlled comparison of the two architectures.


All weights are in the [Kev...