Update README.md

README.md

@@ -32,16 +32,31 @@ This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](http
 You can use this model directly with a pipeline for text classification. Here is an example:
 
 ```python
-from transformers import AutoModel, AutoTokenizer
+import logging
+from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("2nji/llama3-platypus")
-model = AutoModel.from_pretrained("2nji/llama3-platypus")
+# Set up logging
+logging.basicConfig(filename='model_output.log', level=logging.INFO)
 
-inputs = tokenizer("Example input text", return_tensors="pt")
-with torch.no_grad():
-    outputs = model(**inputs)
+# Load the model using the pipeline API for text generation
+model_name = "2nji/llama3-platypus"
+generator = pipeline('text-generation', model=model_name)
 
-print(outputs)
+# Example prompt
+prompt = "Hello! How can AI help humans in daily life?"
+
+# Generate response
+try:
+    responses = generator(prompt, max_length=50)  # Adjust max_length as needed
+    response_text = responses[0]['generated_text']
+    print("Model response:", response_text)
+
+    # Log the output
+    logging.info("Sent prompt: %s", prompt)
+    logging.info("Received response: %s", response_text)
+
+except Exception as e:
+    logging.error("Error in generating response: %s", str(e))
 ```
 
 ## Training and evaluation data