README.md

---
license: apache-2.0
base_model: Qwen/Qwen3.5-2B
base_model_relation: finetune
tags:
- multilingual
- decision-model
- classification
- qwen3_5
- custom-code
- pytorch
- rocm
---

![Open Decision Foundation Models — Sol-2B](assets/decision-sol-2b-header.png)


# Decision-1.0-Sol-2B

*Sol, Latin for sun.*

Give Sol a state, questions and possible answers. It returns decisions and probabilities with labels defined at runtime.


[Decision family](https://huggingface.co/collections/llm-semantic-router/decision-10-6ab12177bd0002394d8409f9)

| Type | Use it for | Output |
|---|---|---|
| **Choice** | Route a request or choose among 2–255 actions. | Selected ID + distribution |
| **Noul** | Check a condition against supplied evidence. | P(true) |
| **Score** | Apply 2–10 ordered rubric descriptions. | Expected index + distribution |

## Measured capability

**66.32% weighted accuracy** across 3,766 decisions and 54 tasks. Compare decision, reading and transfer capabilities in the complete results below.

| Model | Size | Decisions | Composition | Reading | Inference | Transfer | Overall |
|---|---:|---:|---:|---:|---:|---:|---:|
| Sol-2B | 2B | 73.75 | 46.08 | 76.56 | 84.17 | 57.07 | 66.32 |
| Lux-9B | 9B | **84.17** | **52.75** | 89.69 | **91.25** | 77.63 | **77.22** |
| Nox-4B | 4B | **83.00** | **51.79** | 79.06 | **86.25** | 69.60 | **73.09** |
| Kev-9B | 9B | 76.75 | 45.75 | 86.72 | 83.54 | 79.25 | 71.89 |
| Kev-4B | 4B | 71.90 | 48.54 | 81.88 | 84.58 | 76.10 | 70.09 |
| Qwen3.5-9B | 9B | 73.91 | 44.62 | 89.84 | 79.58 | 73.23 | 69.73 |
| Decider | 2B | 64.01 | 46.58 | 92.03 | 84.38 | 69.31 | 67.71 |
| Qwen3.5-4B | 4B | 69.89 | 43.33 | 87.97 | 79.79 | 68.83 | 67.29 |
| Eos-0.8B | 0.8B | 65.94 | 46.04 | 70.31 | 81.67 | 52.01 | 61.89 |
| Kev-0.8B | 0.8B | 60.14 | 42.29 | 67.81 | 68.75 | 61.19 | 58.28 |
| Qwen3.5-2B | 2B | 57.12 | 39.00 | 73.75 | 72.29 | 56.31 | 57.24 |
| Kai-0.6B | 0.6B | 57.96 | 40.83 | 54.69 | 69.79 | 48.37 | 53.52 |
| Laya · English | 0.421B | 56.54 | 35.33 | 51.41 | 63.75 | 53.06 | 51.03 |
| Laya · Multilingual | 0.322B | 47.25 | 38.92 | 50.78 | 57.29 | 47.13 | 47.19 |
| Jev | — | 79.10 | 66.38 | 94.53 | 89.79 | 87.19 | 81.05 |

Accuracy (%). Overall weights: Decisions **30%**, Composition **25%**, Reading **15%**, Inference **15%**, Transfer **15%**. These outcome-informed product-priority weights were chosen after observing results. Bold marks a Decision-family cell above every external open or untuned reference; Jev and other Decision models are excluded.

![Decision model ranking](assets/decision-ranking.png)

![Capability matrix](assets/decision-matrix.png)

[All 54 tasks](TASKS.md) · [Probability, order and missing-evidence diagnostics](DIAGNOSTICS.md) · [Methods and uncertainty](EVALUATION.md)

## More questions, measured

![Question-count latency](assets/decision-question-scaling.png)

Distinct Choice questions at a fixed **499 input tokens per question**. Thirty measurements per point across six independently loaded processes on an otherwise idle AMD gfx942 GPU. Python request latency includes tokenization and inference; loading and network are excluded. [p50, p95 and memory](QUESTION-SCALING.md).

## Use Sol-2B

Use the [official TypeSafe Python SDK](https://docs.typesafe.ai/sdk/python/usage) with your SystemOne-compatible endpoint, configured to serve `Decision-1.0-Sol-2B`. Replace the example URL and API key with your own.

```bash
pip install typesafe-sdk
```

```python
from typesafe_sdk import Choice, Noul, TypeSafeClient

with TypeSafeClient(
    api_key="YOUR_ENDPOINT_API_KEY",
    base_url="https://your-decision-endpoint.example",
    model="Decision-1.0-Sol-2B",
) as client:
    result = client.system_one(
        state="Customer reports a duplicate charge and asks for a refund.",
        questions={
            "route": Choice(
                instructions="Which team should handle this request?",
                criteria={"billing": "Payments and refunds", "technical": "Product faults"},
            ),
            "refund_requested": Noul(instructions="Did the customer request a refund?"),
        },
    )
    print(result.choices["route"].choice)
    print(result.nouls["refund_requested"].noul)
```

The same request with curl:

```bash
curl -X POST 'https://your-decision-endpoint.example/v1/systemone' \
  -H 'Authorization: Bearer YOUR_ENDPOINT_API_KEY' \
  -H 'Content-Type: application/json' \
  --data-raw '{
  "model": "Decision-1.0-Sol-2B",
  "state": "Customer reports a duplicate charge and asks for a refund.",
  "questions": {
    "route": {
      "type": "choice",
      "instructions": "Which team should handle this request?",
      "criteria": {
        "billing": "Payments and refunds",
        "technical": "Product faults"
      }
    },
    "refund_requested": {
      "type": "noul",
      "instructions": "Did the customer request a refund?"
    }
  }
}'
```

[Typed request and response guide](USAGE.md) · [Model runtime requirements](RUNTIME.md)

The complete state, question and candidates must fit 16,384 tokens; overflow is rejected. The bundled normalization profile loads automatically. AMD gfx942 is validated; CPU/MPS are unsupported and NVIDIA is unqualified. Use a fresh Python process when switching profiles.

## Architecture

![Decision decoder architecture](assets/architecture.png)

A causal Qwen3.5 text backbone combines gated linear and full attention. A shared candidate head reads candidate endpoints and the final query vector. Each question uses one forward pass; questions run independently in batches of eight.

[Candidate head](assets/readout.png) · [Vector architecture](assets/architecture.svg) · [Inference code](code/decision_model.py)

Adapted from [Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B). It evaluates supplied evidence without live retrieval; confidence does not guarantee correctness. [License](LICENSE) · [Attributions](ATTRIBUTIONS.md).