update the example for "Inference with Hugging Face library"

LICENSE

@@ -1,63 +0,0 @@
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README.md

@@ -4,24 +4,17 @@ tags:
 - orca
 - orca2
 - microsoft
-license: other
-license_name: microsoft-research-license
-license_link: LICENSE
 ---
 
 # Orca 2
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-Orca 2 is built for research purposes only and provides a single turn response in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization. The model is designed to excel particularly in reasoning.
+Orca 2 is a helpful assistant that is built for research purposes only and provides a single turn response
+in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization.
+The model is designed to excel particularly in reasoning.
 
-Note that:
-
-1. This is a research model, intended to show that we can use capable models and complex workflows (advanced prompts, multiple calls) to create synthetic data that can teach Small Language Models (SLMs) new capabilities. We chose reasoning because it is a widely useful capability that SLMs lack.
-2. The model is not optimized for chat and has not been trained with RLHF or DPO. It is best used after being finetuned for chat or for a specific task.
-3. Beyond reasoning, the model inherits capabilities and limitations of its base (LLAMA-2 base). We have already seen that the benefits of the Orca training can be applied to other base model too.
-
-We make Orca 2's weights publicly available to support further research on the development, evaluation, and alignment of SLMs.
+We open-source Orca 2 to encourage further research on the development, evaluation, and alignment of smaller LMs.
 
 ## What is Orca 2’s intended use(s)?
 
@@ -30,26 +23,25 @@ We make Orca 2's weights publicly available to support further research on the d
 
 ## How was Orca 2 evaluated?
 
-+ Orca 2 has been evaluated on a large number of tasks ranging from reasoning to grounding and safety. Please refer
-to Section 6 and Appendix in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf) for details on evaluations.
++ Orca 2 has been evaluated on a large number of tasks ranging from reasoning to safety. Please refer to Section 6 and Appendix in the paper for details on evaluations.
 
 ## Model Details
 
-Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. 
-All synthetic training data was moderated using the Microsoft Azure content filters. More details about the model can be found in the [Orca 2 paper](https://arxiv.org/pdf/2311.11045.pdf).
+Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was filtered using the Azure content filters. 
+More details about the model can be found at: LINK to Tech Report
 
-Please refer to LLaMA-2 technical report for details on the model architecture.
+Refer to LLaMA-2 for details on model architectures.
 
 ## License
 
-Orca 2 is licensed under the [Microsoft Research License](LICENSE).
+Orca 2 is licensed under the [Microsoft Research License]().
 
 Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.
 
 ## Bias, Risks, and Limitations
 
 Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the
-common limitations of other large language models or limitation caused by its training
+common limitations of other large language models or limitation including by its training
 process, including:
 
 **Data Biases**: Large language models, trained on extensive data, can inadvertently carry
@@ -129,6 +121,7 @@ tokenizer = transformers.AutoTokenizer.from_pretrained(
 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 = "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?"
 
+# 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')
@@ -155,9 +148,9 @@ print(second_turn_answer)
 **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 
-and can help preventing some of content harms. Azure AI Content Safety is a content moderation platform
-that uses AI to moderate content. By having Azure AI Content Safety on the output of Orca 2, 
-the model output can be moderated by scanning it for different harm categories including sexual content, violence, hate, and 
+and can help prevent content harms. Azure AI Content Safety is a content moderation platform
+that uses AI to keep your content safe. By integrating Orca 2 with Azure AI Content Safety, 
+we can moderate the model output by scanning it for sexual content, violence, hate, and 
 self-harm with multiple severity levels and multi-lingual detection. 
 
 ```python
@@ -218,6 +211,7 @@ tokenizer = transformers.AutoTokenizer.from_pretrained(
 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')
@@ -230,16 +224,4 @@ answers = tokenizer.batch_decode(new_output_ids, skip_special_tokens=True)
 final_output = answers[0] if not should_filter_out(answers[0]) else "[Content Filtered]"
 
 print(final_output)
-```
-
-## Citation
-```bibtex
-@misc{mitra2023orca,
-      title={Orca 2: Teaching Small Language Models How to Reason}, 
-      author={Arindam Mitra and Luciano Del Corro and Shweti Mahajan and Andres Codas and Clarisse Simoes and Sahaj Agrawal and Xuxi Chen and Anastasia Razdaibiedina and Erik Jones and Kriti Aggarwal and Hamid Palangi and Guoqing Zheng and Corby Rosset and Hamed Khanpour and Ahmed Awadallah},
-      year={2023},
-      eprint={2311.11045},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI}
-}
 ```