| # FinGPT Compliance Agents - Inference Example | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from peft import PeftModel | |
| import torch | |
| # Load the model | |
| base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct") | |
| model = PeftModel.from_pretrained(base_model, "your-username/fingpt-compliance-agents") | |
| tokenizer = AutoTokenizer.from_pretrained("your-username/fingpt-compliance-agents") | |
| # Example usage | |
| def analyze_financial_text(text): | |
| prompt = f"Analyze this financial text: {text}" | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response | |
| # Test the model | |
| result = analyze_financial_text("Company X reported strong quarterly earnings with 15% revenue growth.") | |
| print(result) | |