evafiai/eva-lender-matching

EVA Lender Matching Model

Fine-tuned Llama 3.2 3B model for SBA lender matching and financial advisory.

Model Description

This model was trained using:

• SFT (Supervised Fine-Tuning): 500 iterations on lender matching data
• DPO (Direct Preference Optimization): 500 iterations for preference alignment

Training Results

Stage	Initial Loss	Final Loss	Improvement
Val Loss	2.902	0.331	88.6% reduction
Train Loss	2.495	0.300	88.0% reduction

Usage

from mlx_lm import load, generate

model, tokenizer = load(
    "mlx-community/Llama-3.2-3B-Instruct-4bit",
    adapter_path="evafiai/eva-lender-matching"
)

prompt = "I need a $500,000 SBA loan for my manufacturing business. What lenders do you recommend?"
response = generate(model, tokenizer, prompt=prompt, max_tokens=300)
print(response)

Capabilities

• SBA 7(a) and 504 loan recommendations
• Lender matching based on business profile
• NAICS code identification
• Industry-specific financing guidance

Training Data

Trained on:

• 7,393 preference pairs for DPO
• Comprehensive lender database with verified contacts
• NAICS code matching examples

License

Apache 2.0