metadata
license: apache-2.0
base_model: mlx-community/Llama-3.2-3B-Instruct-4bit
tags:
- finance
- lender-matching
- mlx
- lora
- dpo
- sba-loans
language:
- en
pipeline_tag: text-generation
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