Update README.md

README.md

@@ -98,6 +98,42 @@ analysis is needed to assess potential harm or bias in the proposed application.
 
 ## Getting started with Orca 2
 
+**Inference with Hugging Face library**
+
+```python
+import transformers
+import torch
+
+model_path = 'microsoft/Orca-2-7b'
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = transformers.AutoModelForCausalLM.from_pretrained(model_path)
+model.to(device)
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(
+    model_path,
+    model_max_length=4096,
+    padding_side="right",
+    use_fast=False,
+    add_special_tokens=False,
+)
+
+system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."
+user_message = "\" \n :You can't just say, \"\"that's crap\"\" and remove it without gaining a consensus. You already know this, based on your block history. —/ \" \nIs the comment obscene? \nOptions : Yes, No."
+
+# We use Chat Markup Language https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/ai-services/openai/includes/chat-markup-language.md#working-with-chat-markup-language-chatml    
+prompt =  f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
+
+inputs = tokenizer(prompt, return_tensors='pt')
+inputs = inputs.to(device)
+
+output_ids = model.generate(inputs["input_ids"], max_length=4096, do_sample=False, temperature=0.0, use_cache=True)
+sequence_length = inputs["input_ids"].shape[1]
+new_output_ids = output_ids[:, sequence_length:]
+answers = tokenizer.batch_decode(new_output_ids, skip_special_tokens=True)
+
+print(answers)
+```
+
 **Safe inference with Azure AI Content Safety**
 
 The usage of [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/) on top of model prediction is strongly encouraged