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use_model.py

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+# Financial LLaMA Model Usage Script
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+
+def load_model(model_path="final_model_continue"):
+    """Load the fine-tuned model"""
+    print("🔧 Loading model...")
+
+    # 4bit quantization configuration
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16
+    )
+
+    # Load base model
+    base_model = AutoModelForCausalLM.from_pretrained(
+        "meta-llama/Meta-Llama-3.1-8B-Instruct",
+        quantization_config=bnb_config,
+        device_map="auto",
+        torch_dtype=torch.bfloat16,
+    )
+
+    # Load LoRA adapter
+    model = PeftModel.from_pretrained(base_model, model_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    print("✅ Model loading completed!")
+    return model, tokenizer
+
+def generate_response(model, tokenizer, prompt, max_length=200):
+    """Generate financial advice response"""
+    inputs = tokenizer(prompt, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_length,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response[len(prompt):]
+
+# Usage example
+if __name__ == "__main__":
+    # Load model
+    model, tokenizer = load_model()
+
+    # Test prompt
+    prompt = """### Instruction:
+Please provide investment advice for investors regarding technology stocks.
+
+### Input:
+A technology company's revenue grew 20% this quarter, but profit margin decreased by 5%, mainly due to increased R&D investment. The company has major breakthroughs in AI.
+
+### Response:"""
+
+    # Generate advice
+    advice = generate_response(model, tokenizer, prompt)
+    print("🤖 AI Investment Advice:")
+    print(advice)