Update README.md

README.md

@@ -25,18 +25,16 @@ Usage
 
 ### Loading the Model
 
-    from transformers import AutoTokenizer, AutoModelForSequenceClassification
+    from transformers import AutoTokenizer, AutoModelForCausalLM
     
     tokenizer = AutoTokenizer.from_pretrained("AventIQ-AI/pythia-410m")
-    model = AutoModelForSequenceClassification.from_pretrained("AventIQ-AI/pythia-410m")
+    model = AutoModelForCausalLM.from_pretrained(model_name, low_cpu_mem_usage=True)
     
     # Example usage
     text = "This product is amazing!"
     inputs = tokenizer(text, return_tensors="pt")
-    outputs = model(**inputs)
-    logits = outputs.logits
-    predicted_class = logits.argmax().item()
-    print("Predicted class:", predicted_class)
+    outputs = model.generate(**inputs, max_new_tokens=20)
+    print(tokenizer.decode(original_model_output[0], skip_special_tokens=True))
 
 Performance Metrics
 -------------------