from mlx_lm import load, generate

# Load base model with adapters
model, tokenizer = load(
    "mlx-community/meta-llama-Llama-4-Scout-17B-16E-4bit",
    adapter_path="evafiai/eva-llama4-scout-financial-lora"
)

# Generate
prompt = "What is a DSCR loan and what are typical requirements?"
response = generate(model, tokenizer, prompt=prompt, max_tokens=500)
print(response)

Usage with Transformers + PEFT

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-4-Scout-17B-16E-Instruct",
    device_map="auto",
    torch_dtype="auto"
)

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "evafiai/eva-llama4-scout-financial-lora")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct")

# Generate
inputs = tokenizer("What is DSCR?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))

Capabilities

Credit Analysis: Evaluate creditworthiness, FICO scores, tradelines
Loan Matching: Match borrowers to appropriate lenders and products
DSCR Calculations: Debt Service Coverage Ratio analysis
Underwriting: Guidelines interpretation and documentation requirements
Financial Terminology: Definitions and explanations of industry terms

Limitations

Optimized for commercial lending scenarios
Best used with relevant context/RAG for specific lender products
May require domain-specific prompting for best results

License

This model inherits the Llama 3.1 Community License from the base model.

Citation

@misc{eva-financial-ai-2024,
  title={EVA Financial AI - Llama 4 Scout LoRA Adapters},
  author={EVA Financial AI},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/evafiai/eva-llama4-scout-financial-lora}
}