Upload inference_example.py with huggingface_hub

inference_example.py

@@ -0,0 +1,24 @@
+
+# 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)