sba-extract-v4

sba-extract-v4 — SBA Form 413 Field Extractor

A fine-tuned Qwen2.5-3B model that extracts 16 financial fields from US tax returns into structured JSON for SBA Form 413 (Personal Financial Statement). Built for community banks and credit unions automating small business lending.

What it does

Feed it a tax return (text or markdown) → get structured JSON:

{
  "name": "John M Smith",
  "ssn": "123-45-6789",
  "employer": "ACME Technologies Inc",
  "annual_income": 125000,
  "cash": 120000,
  "real_estate": 650000,
  "mortgages": 450000,
  "stocks_bonds": 120000,
  "retirement_accounts": 250000,
  "life_insurance": 35000,
  "notes_receivable": 10000,
  "credit_card_debt": 8500,
  "auto_loans": 25000,
  "total_assets": 1185000,
  "total_liabilities": 483500,
  "net_worth": 701500
}

Accuracy

The 3 partial misses are multi-value fields (e.g., two bank accounts, two properties) — the model picks the first value. The Forms SBA platform fixes this with a post-processing sum-of-parts rule.

Model details

Quick start

Option 1: Ollama

ollama pull hf.co/newdim001/sba-extract-v4
ollama run sba-extract-v4

Option 2: llama.cpp

llama-cli \
  -m sba-extract-v4.gguf \
  -p "Extract SBA Form 413 fields from: FORM 1040 - John Smith SSN 123-45-6789 Wages: 125000 Cash: 45000..."

Option 3: Python (recommended prompt)

prompt = """
### Instruction:
Extract ALL SBA Form 413 fields from the tax return below. Return ONLY valid JSON with these 16 fields:
- name, ssn, employer, annual_income, cash, real_estate, mortgages
- stocks_bonds, retirement_accounts, life_insurance, notes_receivable
- credit_card_debt, auto_loans, total_assets, total_liabilities, net_worth

### Input:
{your_tax_return_text}

### Response:
"""

Built by

Koodo Technology LLC FZ (Dubai) — part of the Forms SBA platform. Automated SBA lending for US community banks and credit unions.

License

MIT