Decision-1.0-Kai-0.6B

Kai, from kairos — the right moment to choose.

Open Decision Foundation Models

Choose an action, judge a condition, or score against your own rubric. Kai reads the context and candidate descriptions together, then returns structured decisions and probability distributions.

Decision collection · Download

Measured decisions

53.52 overall — above both general Laya models, and +7.04 points over the previous Kai release.

On the benchmark's 160-question BoolQ slice, Kai scores 74.38%, versus 69.38% for each Laya reference.

Same 54 tasks and 3,766 scored questions, with fixed source/family weights. Both Laya references use released general weights, without target-dataset fine-tuning. This is an observed regression suite; results vary by task. Full matrix, paired intervals, and methods.

Three ways to decide

Use the System One format: state / model / questions → answers. Ask many questions about one context, or apply shared questions across a batch of contexts. Candidates are supplied at runtime.

SDK and curl examples · Training-source attribution

128 mixed questions in 163 ms — 58% lower latency. Automatic typed scheduling accelerates the measured SystemOne runtime with the same weights. Paired local AMD measurements on a fixed workload. Latency and scaling.

Download for local inference

hf download vllm-sr/Decision-1.0-Kai-0.6B --local-dir Decision-1.0-Kai-0.6B

This repository contains model files, provenance and the Transformers loading code. Serving requires a compatible vLLM Semantic Router Decision runtime, distributed separately. Check its hardware support before serving. The runtime must support vllm-sr-decision format version 1 and the file map in config.json. For local inference without a server, see Use with 🤗 Transformers below.

Use with 🤗 Transformers

The repository includes its inference code, so stock Transformers can download and run the complete model locally with trust_remote_code=True. system_one takes and returns the same System One request and response bodies as the Decision runtime; nothing is generated.

pip install "transformers>=4.57" torch safetensors huggingface_hub

from transformers import AutoModel

model = AutoModel.from_pretrained("vllm-sr/Decision-1.0-Kai-0.6B", trust_remote_code=True)
response = model.system_one(
    state="The parcel arrived damaged. Please send a replacement today.",
    questions={
        "route": {
            "type": "choice",
            "instructions": "Which team should handle this request?",
            "criteria": {"delivery": "Damaged or missing parcels", "billing": "Payments and invoices"},
        },
        "urgent": {
            "type": "noul",
            "instructions": "Does the customer request action today?",
        },
    },
)
print(response["answers"]["route"]["choice"], response["answers"]["urgent"]["noul"])

pipeline("decision", model="vllm-sr/Decision-1.0-Kai-0.6B", trust_remote_code=True) accepts the same request body. A malformed question is answered with an invalid_question error. The model loads on the first GPU when one is visible, otherwise on the CPU (pass device="cpu" or device="cuda:0" to choose); weights and arithmetic are FP32. A complete question, its candidates and the state are limited to 1,024 tokens; if a question is longer, every question of the request is answered with a max_length_exceeded error and nothing is truncated.

Use

Replace the placeholder with a SystemOne-compatible endpoint configured to serve Decision-1.0-Kai-0.6B, and set DECISION_API_KEY to that endpoint's key.

pip install typesafe-sdk

import os
from typesafe_sdk import TypeSafeClient, Choice, Noul

client = TypeSafeClient(
    api_key=os.environ["DECISION_API_KEY"],
    base_url="https://your-decision-endpoint.example",
    model="Decision-1.0-Kai-0.6B",
)
questions = {
    "route": Choice(instructions="Which team should handle this request?",
                    criteria={"delivery": "Damaged or missing parcels", "billing": "Payments and invoices"}),
    "urgent": Noul(instructions="Does the customer request action today?"),
}
response = client.system_one(state="The parcel arrived damaged. Please send a replacement today.", questions=questions)
print(response.choices["route"].choice, response.nouls["urgent"].noul)

The same request with curl:

curl -X POST https://your-decision-endpoint.example/v1/systemone \
  -H "Authorization: Bearer $DECISION_API_KEY" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "Decision-1.0-Kai-0.6B",
    "state": "The parcel arrived damaged. Please send a replacement today.",
    "questions": {
      "route": {"type": "choice", "instructions": "Which team should handle this request?", "criteria": {"delivery": "Damaged or missing parcels", "billing": "Payments and invoices"}},
      "urgent": {"type": "noul", "instructions": "Does the customer request action today?"}
    }
  }'

Official Python SDK · HTTP API

Architecture

Three 22-layer bidirectional paths share multilingual input embeddings. Each decision type has its own interaction layers and candidate readout. Candidates within a question are scored together; questions are processed in batches.

Choice and Score were updated while preserving the released Noul path exactly. The model files retain the three-path architecture and complete-input contract.

Architecture and readout diagrams · Training and release notes

The complete 1,024-token budget includes context, questions, candidates, and special tokens. Longer inputs are rejected. This release's headline evaluation includes English and Chinese; broader multilingual results from previous weights are historical evidence.

Transfer remains behind Laya English; Reading is 1.25 points below the previous Kai release. Probabilities are not guarantees, and candidate order can affect predictions. Full results and limitations · AMD runtime measurements

Built on Vela Encoder. Attribution · License scope