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
manifest.json
router/
thresholds.json
model/
planner/
system_prompt.txt
adapter/
Components
Router:
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:
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:
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:
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.