A LoRA adapter on top of Qwen2.5-1.5B-Instruct that performs structured content-moderation analysis for IC-design industry text (mixed Traditional Chinese + English). It outputs:
Jump from L1 to L2 demonstrates that the fine-tuning learned the task; L2 ≈ L3 means the adapter's output is already clean enough that the universal filters don't need to intervene on in-distribution data (filters retained as safety net).
Inference safeguards
Output passes through three augmentation layers:
Schema validation — enforces legal enum values
Provenance check — every entity's value must appear verbatim in the input (anti-hallucination)
User dictionary/regex augmentation (offered via demo Space) — users can plug in their own customer lists, vendor lists, codenames, and pattern regex. Augments entities and categories only; does not override risk_level.
Limitations
All training data is synthetic; no real company data was used
Holdout shares the synthesized distribution; real-world OOD performance untested
CPU inference is slow (~90–120s per query); production deployment should consider GGUF Q4 quantization or GPU
Open-vocabulary entities (new codenames the model has never seen) rely on LLM semantic inference and may benefit from dictionary augmentation
License
Apache 2.0. The model weights, inference code, and demo are all released under Apache 2.0.
Disclaimer
Training data: All 600 training samples and 99 holdout samples were synthesized via template + slot-filling. The default demo dictionary in the companion Space uses fictional placeholder names (e.g., Customer-A, Vendor-Foundry-X, Codename-Alpha); none refer to any real company, vendor, or project. Users deploying this model should replace the demo dictionary with their own organization's actual lists. The model's outputs do not represent any specific company, vendor, or project. The authors disclaim any responsibility for misuse.
