Update README.md

README.md

@@ -105,9 +105,7 @@ tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
 messages = [
     {"role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user."},
     {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
-    {"role": "system", "content": "Sure! Here are some ways to eat bananas and dragonfruits together:"},
-    {"role": "system", "content": "1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey."},
-    {"role": "system", "content": "2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
+    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
     {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
 ]
 
@@ -132,7 +130,7 @@ Note that by default the model use flash attention which requires certain types
 
 + V100 or earlier generation GPUs: call `AutoModelForCausalLM.from_pretrained()`  with `attn_implementation="eager"`
 + CPU: use the **GGUF** quantized models [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf)
-+ Optimized inference: use the **ONNX** models [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx)
++ Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx)
 
 ## Responsible AI Considerations