Model Card

This model is a fine-tuned version of unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit.

The fine-tuning objective is to reduce hallucinations when an LLM is asked to map free-form construction job descriptions to the most appropriate equipment/work-type category.

Model Details

Base model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
Fine-tuning method: LoRA + SFT (PEFT / TRL)
Task type: Domain classification-style text generation
Primary domain: Construction permits / job description interpretation

Intended Use

Convert user-provided job descriptions into grounded, domain-relevant categories.
Improve consistency and reduce fabricated outputs versus the base model.
Support permit-related assistant workflows where structured label output is needed.

Out-of-Scope Use

Legal, compliance, or safety decisions without human review.
Use outside construction/permit-like text domains without additional validation.
High-stakes automation where incorrect classifications can cause harm.

Training Data

Fine-tuned on a sampled subset of NYC DOB permit records focused on:

Job Description
Work Type

Placeholder links to source datasets:

Primary dataset (placeholder): https://data.cityofnewyork.us/Housing-Development/DOB-NOW-Build-Approved-Permits/rbx6-tga4/about_data

Evaluation

The current notebook workflow tracks:

Accuracy
ROUGE-1
ROUGE-2
ROUGE-L

Please replace with run-specific values when publishing:

Accuracy: 81.94%
ROUGE-1: 82.03%
ROUGE-2: 53.57%
ROUGE-L: 82.03%

Limitations

Can still hallucinate or over-generalize on unseen phrasing.
Sensitive to class imbalance and noisy descriptions.
Should be monitored for false positives across similar equipment/work types.

Get Started

Use the fine-tuned adapter/checkpoint from your training outputs and run inference with your existing prompt template for job-description classification.

Framework Versions

PEFT 0.18.1

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