dhtocks / malkuth-2b

Malkuth-2B

A small multilingual decision model for classification, with a focus on Korean.

Given a text, a question, and a set of criteria, Malkuth returns probabilities over the possible choices instead of generating text.

Malkuth-2B is a LoRA adapter trained on Kev and built on a frozen empero-ai/Qwen3.8-2B-Distill . It has 17.9M trainable parameters and a 65 MB adapter.

It supports three question types:

• choice : select one of up to 255 options
• noul : yes or no
• score : select an ordered level

Multiple questions can be evaluated against the same input. Questions are evaluated independently, so they cannot see each other's answers. Probabilities are temperature-calibrated on a held-out validation split.

Malkuth is served through Kev and works with the TypeSafe System One API. The companion model is dhtocks/malkuth-4b (4B). Code, training mixes and evaluation live in newfull5/malkuth .

Usage

git clone https://github.com/jaredpalmer/kev.git kev && cd kev && uv sync --extra serve
uv run --extra serve python -m kev.serve --run dhtocks/malkuth-2b --port 8009

The official typesafe-sdk works against the local server with a base_url change.

from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

client = TypeSafeClient(api_key="local", base_url="http://127.0.0.1:8009", model="kev-latest")
r = client.system_one(
    state="Shoes arrived two weeks late and in the wrong size. I also see two charges on my card.",
    questions={
        "team": Choice(instructions="Which team should handle this?",
                       criteria={"billing": "Charges and invoices", "shipping": "Delivery problems",
                                 "returns": "Exchanges and wrong items"}),
        "escalate": Noul(instructions="Does this need urgent human attention?"),
        "urgency": Score(instructions="How urgent is this ticket?", criteria=["low", "normal", "high"]),
    },
)
print(r.choices["team"].choice, r.choices["team"].probabilities)

Evaluation

29 suites and about 129,000 questions. Every model answered the same items. Jev was measured through its API.

• held-out : the nine datasets we did not train on
• all : all 29 suites, ten of which we trained on
• Brier : quality of the probabilities, lower is better

On the five Korean suites:

Four of these five contributed their train splits to our mix, so this table is in-distribution for Malkuth.

Latency on one A100 80GB in bf16, median of 20 requests. The second number is a repeated input served from the prefix cache. Six questions take 23 ms on a new input and 16 ms on a repeated one.

Per-dataset scores and calibration analysis are in docs/evaluation.md .

Training data

89,791 labelled requests from 20 public sources. 62,400 choice , 24,790 noul and 7,401 score questions, across ten core languages with five Korean-only sources.

Built by tools/build_train_mix.py . Per-source licences are in docs/benchmarks.md .

License

• Weights: research use only. Two of the twenty training sources are non-commercial: XNLI (CC-BY-NC-4.0) and RACE ("non-commercial research purpose only"). sms_spam and tweet_sentiment_multilingual state no licence.
• Code: Apache-2.0, same as Kev and the Qwen bases.

Citation

@misc{malkuth2026,
  title={Malkuth: multilingual System One decision models},
  author={Saechan Oh},
  year={2026}
}