---
license: other
base_model:
- microsoft/deberta-v3-small
- mlx-community/Qwen3-4B-Instruct-2507-4bit
tags:
- knowledge-graph
- query-planning
- cypher
- transformers
- mlx
- qlora
---

# Business Model KG Query Stack

This repository contains the deployable local query stack for **Business Model
KG**. It is not a standalone chatbot model. It is a runtime bundle used by the
project to decide how a question should be handled and, when safe, to produce a
structured local query plan.

The bundle combines:

- a fine-tuned DeBERTa-v3-small router classifier
- calibrated router thresholds
- an MLX QLoRA planner adapter for `mlx-community/Qwen3-4B-Instruct-2507-4bit`
- the frozen planner system prompt
- the runtime bundle manifest

The package intentionally excludes optimizer states and intermediate training
checkpoints. It is the deployable bundle, not the full training workspace.

## Layout

```text
manifest.json
router/
  thresholds.json
  model/
planner/
  system_prompt.txt
  adapter/
```

## Components

Router:

```text
microsoft/deberta-v3-small
```

The router is stored as a full fine-tuned classifier under `router/model/`.
It predicts whether a question is suitable for local answering, should fall back
to a stronger hosted model, or should be refused. Runtime thresholds are stored
in `router/thresholds.json`.

Planner:

```text
mlx-community/Qwen3-4B-Instruct-2507-4bit
```

The planner is stored as an adapter only under `planner/adapter/`. To use it, the
runtime also needs the base model above. The planner system prompt is included in
`planner/system_prompt.txt`.

## Runtime Role

When the router selects the local path, the planner emits a compact supported
query plan. The Business Model KG Python runtime then validates that plan and
compiles it into read-only Cypher. This separation keeps the planner constrained
to the project ontology and avoids letting it execute arbitrary database logic.

## Intended Use

Use this repository as the contents of:

```text
runtime_assets/query_stack/
```

inside the Business Model KG project. The expected file locations are described
by `manifest.json`.

## Training Data

The related training dataset is published separately as:

```text
WindyITS/business-model-kg-query-planner-data
```

The router was trained on 8,000 examples and validated on 1,200 examples across
the labels `local`, `api_fallback`, and `refuse`.

No standalone license has been selected for this release yet. Check the upstream
terms for the base models before redistribution or production use.