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 Qwen3.8 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

• Yes/no (noul), multiple-choice (choice) and rating (score) questions in one request. The questions share the text but can't read each other.
• Calibrated probabilities by default: each checkpoint ships with a fitted temperature.
• Drop-in for Jev: the TypeSafe Python SDK works against a Kev server unchanged.
• Four sizes, versioned together as Kev 1.0: from a 0.8B that runs on a laptop to a 27B for a single data-centre GPU.
• Documents of up to 65,536 tokens, on CUDA and on Apple Silicon through MLX. Each model card says how long a document can get before accuracy drops.
• Fine-tune on your own labelled examples. A coding-agent skill runs the whole loop on Modal, from finding your questions to serving the result.
• Deploy your own HTTPS endpoint with one command. It scales to zero when idle.
• Try it in the browser first: huggingface.co/spaces/jaredpalmer/kev.

Models

Start with Kev-4B. Move to Kev-9B if you have a bigger GPU, or to Kev-27B if you have an 80 GB GPU and want the most accurate Kev. Use Kev-0.8B when size matters more than accuracy.

Model	Base (license)	Runs on: CUDA	Runs on: Mac (MLX)	Validated context	Held-out datasets: index	Card
Kev-0.8B	Qwen3.5-0.8B-Base (Apache-2.0)	L4, any 4 GB GPU	Any Apple Silicon Mac; measured to 65k tokens	8,192	23.3	Details
Kev-4B	Qwen3.5-4B-Base (Apache-2.0)	L40S, H100	32 GB Mac; measured to 65k tokens	8,192	38.0	Details
Kev-9B	Qwen3.5-9B-Base (Apache-2.0)	L40S, H100	32 GB Mac or larger (expected, not measured)	8,192	41.0	Details
Kev-27B	Qwen3.8-27B, post-trained (Apache-2.0)	B200, H200, H100 80 GB	96–128 GB Mac (expected, not measured)	65,536	52.3	Details
Jev	Hosted	TypeSafe's API	–	–	54.0	–

"Held-out datasets" is the chance-corrected index of the community Decision Index, scored on the test split of breadth-v1: 14 public datasets in five areas that no Kev trained on. "Validated context" is the longest document, in tokens, for which accuracy on real contracts (CUAD) stays within 3 points of the same model's accuracy at 8k tokens, at the 95 % lower bound; each model card has the measurement by length.

Model	Accuracy: New Sources	Accuracy: Trained Sources	Brier: New Sources
Kev-0.8B	0.648 / 0.697	0.827 / 0.838	0.481 / 0.416
Kev-4B	0.817 / 0.838	0.873 / 0.865	0.269 / 0.242
Kev-9B	0.820 / 0.852	0.874 / 0.873	0.289 / 0.217
Kev-27B	0.851 / 0.889	0.865 / 0.866	0.225 / 0.156
Jev	0.857 / –	0.845 / –	0.211 / –

Each cell is development / test. "New sources" means datasets and policy rules Kev never saw during training. It is the closest thing here to your own questions. "Trained sources" means held-out examples from the datasets Kev was trained on. We pick checkpoints using the development sets and read each test set only once per released model. Jev has only been run on the development sets of these two suites. Brier scores the whole probability distribution, not just the top answer; lower is better.

On new sources Kev-27B is within a point of Jev (0.851 vs 0.857), and Kev-4B and Kev-9B are within four points. We don't know what Jev was trained on, so this isn't a controlled comparison of the two architectures. What to Expect says where Kev is as good as Jev and where it isn't.

Kev-0.8B, 4B and 9B start from Qwen base models and share one training recipe: a small adapter on a frozen base. Kev-27B starts from Qwen's post-trained release, and we don't know what that was trained on; every one of its weights is fine-tuned, so it ships as 51 GB of full weights rather than an adapter. Each model card has the full recipe, all results, and the earlier versions kept as Hub tags.

Kev 1.0

The four models above are released together as Kev 1.0. Each Hub repo has a v1.0 tag, so --run jaredpalmer/[email protected] always loads the same weights, and the GitHub release kev-1.0 has the 0.8B, 4B and 9B checkpoints with SHA-256 checksums. Kev-27B's 51 GB of weights are too large for a release asset and are on the Hub only.

Model	Hub revision of the weights	Temperature	Trained on states up to
Kev-0.8B	9a45d25e	2.35	7,552 tokens
Kev-4B	139fdd94	2.41	7,552 tokens
Kev-9B	b5d8c18e (v2)	2.19	7,552 tokens
Kev-27B	28be62e9 (v2, full weights)	1.32	32,768 tokens

Kev 1.0 trains nothing new. It fixes the checkpoints, cards, evaluation suites and serving code that the next generation of Kev will be compared against. The release notes list what changed since the previous family release and what is known not to work well.

Quick Start

Try It in the Browser

The Hugging Face Space runs Kev-4B and Kev-0.8B, with nothing to install.

This is the beginning of the README. Read the rest at the original. Read the full README ↗