update readme

README.md

@@ -18,13 +18,13 @@ library_name: transformers
  
 Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures.
  
-🏡 [Phi-4-mini Portal]() <br>
+🏡 [Phi-4-mini Portal](https://azure.microsoft.com/en-us/products/phi) <br>
 📰 [Phi-4-mini Microsoft Blog](https://aka.ms/phi4techblog-feb2025) <br>
 📖 [Phi-4-mini Technical Report]() <br>
 👩‍🍳 [Phi-4-mini Cookbook]() <br>
 🖥️ [Try It](https://aks.ms/try-phi4mini) <br>
  
-**Phi-4-mini**: [[mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) | [onnx]()]; [[multimodal-instruct]](https://huggingface.co/microsoft/Phi-4-multimodal-instruct);
+**Phi-4-mini**: [[mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-mini-instruct-onnx)]; [[multimodal-instruct]](https://huggingface.co/microsoft/Phi-4-multimodal-instruct);
  
 ## Intended Uses
  
@@ -173,14 +173,16 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
  
 torch.random.manual_seed(0)
- 
+
+model_path = "microsoft/Phi-4-mini-instruct"
+
 model = AutoModelForCausalLM.from_pretrained(
-    "microsoft/Phi-4-mini-instruct",
-    device_map="cuda",
+    model_path,
+    device_map="auto",
     torch_dtype="auto",
     trust_remote_code=True,
 )
-tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-4-mini-instruct")
+tokenizer = AutoTokenizer.from_pretrained(model_path)
  
 messages = [
     {"role": "system", "content": "You are a helpful AI assistant."},