Karpo Decision 0.8B

A decision model of the Karpo Decision family: given a state (a web page, a document, a conversation: any text or JSON) and typed questions about it, it returns a probability for every option of every question in one forward pass, without generating text. Fine-tuned from Qwen/Qwen3.5-0.8B, with a linear readout over the options.

Parameters: 0.85B in bf16, plus a 255 × 1,024 readout in fp32
Questions: choice (up to 255 options), noul (true or false), score (a level on a scale)
Inputs: text or JSON, and a screenshot at state.viewport_screenshot
Context: 32,768 tokens per sequence; a longer request is split by questions, each part with the full state
Interfaces: a Python API and an HTTP server in the System One format (POST /v1/systemone)
Licence: MachinePulse Community License: commercial use is allowed; commercial products and services offered to third parties must show "Powered by karpo-decision (MachinePulse)"; production use for commercial purposes by an entity whose annual revenue, with its affiliates, exceeds US$10M needs a commercial agreement

The Karpo Decision family

Score vs. size: Karpo Decision 0.8B to 31B on the Decision Index 0.3 public suite, with the other board models of each size class

Score vs. size on the Decision Index 0.3 public suite, board of 12 November 2026.

Model	Base	Parameters
karpo-decision-31B	Gemma 4 31B	31.3B
karpo-decision-12B	Gemma 4 12B	12.0B
karpo-decision-9B	Qwen3.5 9B	8.4B
karpo-decision-4B	Qwen3.5 4B	4.5B
karpo-decision-2B	Qwen3.5 2B	2.2B
karpo-decision-0.8B	Qwen3.5 0.8B	0.85B

The family's largest model, karpo-decision-31B, is ranked #1 on the Decision Index, with the highest Full score, 73.9, on the maintainers' private tests (edition 0.4.1, board of 12 November 2026, 300 entries); its public index is 75.0.

How it works

A readout, not generation. Every option is labelled with an option code. At the readout point that ends each question, a linear readout scores 255 option codes and a softmax over the question's own options gives the probabilities. Nothing is decoded, so the answer is always one of the options.
Many questions, one forward pass. A request is one state and any number of questions, encoded as one sequence: the list of questions, the state, then each question again with its options and a readout point.
Hybrid attention. The full-attention layers read the whole sequence in both directions; the linear-attention layers read it in order, so the questions come before the state.

The question-list-first layout follows FlexDM and jev-brain.

Use

Inference code: github.com/machinepulse-ai/karpo-decision, with the qwen extra. Python 3.12 or later and one GPU; the weights take 1.7 GB, long requests more. Loading the checkpoint as a plain chat model does not reproduce its answers.

pip install "karpo-decision[qwen] @ git+https://github.com/machinepulse-ai/karpo-decision"

from huggingface_hub import snapshot_download
from karpo_decision.decider import Decider

path = snapshot_download("MachinePulseAI/karpo-decision-0.8B")
decider = Decider.from_pretrained(path, device="cuda")
response = decider.decide({
    "state": "Order 1182: the customer asks to return a blender bought 45 days ago. Policy: returns within 30 days.",
    "questions": {
        "eligible": {"type": "noul", "instructions": "Is the return eligible under the policy?"},
        "action": {"type": "choice", "instructions": "What should the agent do?",
                   "criteria": {"refund": "Issue a full refund", "decline": "Politely decline the return",
                                "escalate": "Escalate to a supervisor"}},
    },
})
print(response["answers"])
# {"eligible": {"type": "noul", "noul": P(true)},
#  "action": {"type": "choice", "choice": <key>, "probabilities": {<key>: p, ...}, "confidence": c}}

A screenshot goes in the state at viewport_screenshot, as {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}.

HTTP server (POST /v1/systemone, GET /healthz):

CKPT=$(python -c 'from huggingface_hub import snapshot_download; print(snapshot_download("MachinePulseAI/karpo-decision-0.8B"))')
torchrun --standalone --nproc_per_node 1 -m karpo_decision.serve.server --checkpoint "$CKPT" --port 18440

Decision Index kit, installed separately:

pip install "decision-index @ git+https://github.com/apolinario/decision-index"
python -m decision_index run --engine karpo_decision.serve.engine:DecisionIndexEngine \
    --option checkpoint="$CKPT" --out runs/karpo-decision-0.8B

Lineage

Qwen/Qwen3.5-0.8B-Base → Qwen/Qwen3.5-0.8B → karpo-decision-0.8B (fine-tuned)

Limitations

Images: a screenshot is read through the base model's vision tower; image inputs were not evaluated.
Not a chat or text-generation model; generation was not evaluated.
A question that does not fit in 32,768 tokens with its state is refused, never truncated.
It always picks one of the given options.

Licence

The weights and model files are licensed under the MachinePulse Community License; this summary is for convenience and the LICENSE governs. Commercial use is allowed. Commercial products and services offered to third parties, paid or free, must carry the attribution below; internal use and internal tools, evaluation, testing, benchmarking, research, and pilots of up to 90 days that serve no paying customers need none. Production use for commercial purposes by an entity whose annual revenue, with its affiliates, exceeds US$10M needs a separate commercial agreement: write to contact@machinepulse.ai with the subject line "Karpo Decision licensing: commercial agreement request". Models fine-tuned, merged, quantized, pruned or distilled from these weights stay under the same licence; a model trained only on this model's outputs does not.

karpo-decision-0.8B is a modified version of Qwen/Qwen3.5-0.8B (Apache-2.0): decoder weights fine-tuned, readout added. Qwen3.5 stays under its own licence, a copy of which is in licenses/Qwen3.5-LICENSE; see NOTICE.

The inference code (github.com/machinepulse-ai/karpo-decision) is licensed under Apache-2.0.

Attribution

Commercial products and services offered to third parties that use this model or a derivative must show, on a related website, user interface, about page or product documentation: