update: README.md

README.md

@@ -137,6 +137,9 @@ outputs = model.generate(**inputs, max_new_tokens=5)
 response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 print("Model Response:", response)
+
+# Example output
+Model Response: True Positive
 ```
 
 ## Training Details
@@ -183,10 +186,9 @@ The model demonstrates high accuracy in classifying Wazuh alerts but may require
 
 ## Environmental Impact
 
-- **Hardware Type:** A100 GPU
-- **Hours Used:** 15
+- **Hardware Type:** T4 GPU
+- **Hours Used:** 5
 - **Cloud Provider:** GCP
-- **Compute Region:** us-central1
 - **Carbon Emitted:** Estimated using [ML Impact Calculator](https://mlco2.github.io/impact#compute)
 
 ## Technical Specifications