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--- |
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pipeline_tag: text-generation |
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tags: |
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- orca |
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--- |
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# Orca 2 |
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<!-- Provide a quick summary of what the model is/does. --> |
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Orca 2 is a helpful assistant that is built for research purposes only and provides a single turn response |
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in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization. |
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The model is designed to excel particularly in reasoning. |
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We open-source Orca 2 to encourage further research on the development, evaluation, and alignment of smaller LMs. |
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## What is Orca’s intended use(s)? |
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+ Orca 2 is built for research purposes only. |
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+ The main purpose is to allow the research community to assess its abilities and to provide a foundation for building better frontier models. |
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## How was Orca evaluated? |
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+ Orca 2 has been evaluated on a large number of tasks ranging from reasoning to safety. Please refer to Sections 6, 7, 8, 9, 10, and 11 in the paper for details about different evaluation experiments. |
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## Model Details |
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Refer to LLaMA-2 for details on model architectures. |
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Orca 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. |
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More details about the model can be found at: LINK to Tech Report |
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## License |
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The model is licensed under the [Microsoft Research License](). |
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Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved. |
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## Uses |
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## Bias, Risks, and Limitations |
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Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the |
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common limitations of other large language models or limitation including by its training |
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process, including: |
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**Data Biases**: Large language models, trained on extensive data, can inadvertently carry |
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biases present in the source data. Consequently, the models may generate outputs that could |
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be potentially biased or unfair. |
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**Lack of Contextual Understanding**: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting |
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in potential inaccuracies or nonsensical responses. |
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**Lack of Transparency**: Due to the complexity and size, large language models can act |
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as “black boxes”, making it difficult to comprehend the rationale behind specific outputs or |
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decisions. We recommend reviewing transparency notes from Azure for more information. |
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**Content Harms**: There are various types of content harms that large language models |
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can cause. It is important to be aware of them when using these models, and to take |
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actions to prevent them. It is recommended to leverage various content moderation services |
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provided by different companies and institutions. On an important note, we hope for better |
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regulations and standards from government and technology leaders around content harms |
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for AI technologies in future. We value and acknowledge the important role that research |
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and open source community can play in this direction. |
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**Hallucination**: It is important to be aware and cautious not to entirely rely on a given |
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language model for critical decisions or information that might have deep impact as it is |
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not obvious how to prevent these models from fabricating content. Moreover, it is not clear |
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whether small models may be more susceptible to hallucination in ungrounded generation |
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use cases due to their smaller sizes and hence reduced memorization capacities. This is an |
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active research topic and we hope there will be more rigorous measurement, understanding |
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and mitigations around this topic. |
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**Potential for Misuse**: Without suitable safeguards, there is a risk that these models could |
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be maliciously used for generating disinformation or harmful content. |
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**Data Distribution**: Orca 2’s performance is likely to correlate strongly with the distribution |
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of the tuning data. This correlation might limit its accuracy in areas underrepresented in |
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the training dataset such as math, coding, and reasoning. |
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**System messages**: Orca 2 demonstrates variance in performance depending on the system |
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instructions. Additionally, the stochasticity introduced by the model size may lead to |
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generation of non-deterministic responses to different system instructions. |
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**Zero-Shot Settings**: Orca 2 was trained on data that mostly simulate zero-shot settings. |
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While the model demonstrate very strong performance in zero-shot settings, it does not show |
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the same gains of using few-shot learning compared to other, specially larger, models. |
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**Synthetic data**: As Orca 2 is trained on synthetic data, it could inherit both the advantages |
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and shortcomings of the models and methods used for data generation. We posit that Orca |
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2 benefits from the safety measures incorporated during training and safety guardrails (e.g., |
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content filter) within the Azure OpenAI API. However, detailed studies are required for |
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better quantification of such risks. |
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This model is solely designed for research settings, and its testing has only been carried |
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out in such environments. It should not be used in downstream applications, as additional |
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analysis is needed to assess potential harm or bias in the proposed application. |
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## Getting started with Orca 2 |
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**Safe inference with Azure AI Content Safety** |
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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 |
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and can help prevent content harms. Azure AI Content Safety is a content moderation platform |
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that uses AI to keep your content safe. By integrating Orca 2 with Azure AI Content Safety, |
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we can moderate the model output by scanning it for sexual content, violence, hate, and |
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self-harm with multiple severity levels and multi-lingual detection. |
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```python |
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import os |
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import math |
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import transformers |
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import torch |
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from azure.ai.contentsafety import ContentSafetyClient |
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from azure.core.credentials import AzureKeyCredential |
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from azure.core.exceptions import HttpResponseError |
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from azure.ai.contentsafety.models import AnalyzeTextOptions |
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CONTENT_SAFETY_KEY = os.environ["CONTENT_SAFETY_KEY"] |
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CONTENT_SAFETY_ENDPOINT = os.environ["CONTENT_SAFETY_ENDPOINT"] |
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# We use Azure AI Content Safety to filter out any content that reaches "Medium" threshold |
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# For more information: https://learn.microsoft.com/en-us/azure/ai-services/content-safety/ |
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def should_filter_out(input_text, threshold=4): |
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# Create an Content Safety client |
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client = ContentSafetyClient(CONTENT_SAFETY_ENDPOINT, AzureKeyCredential(CONTENT_SAFETY_KEY)) |
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# Construct a request |
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request = AnalyzeTextOptions(text=input_text) |
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# Analyze text |
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try: |
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response = client.analyze_text(request) |
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except HttpResponseError as e: |
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print("Analyze text failed.") |
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if e.error: |
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print(f"Error code: {e.error.code}") |
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print(f"Error message: {e.error.message}") |
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raise |
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print(e) |
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raise |
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categories = ["hate_result", "self_harm_result", "sexual_result", "violence_result"] |
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max_score = -math.inf |
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for category in categories: |
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max_score = max(max_score, getattr(response, category).severity) |
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return max_score >= threshold |
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def run_inference(model_path, inputs): |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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model = transformers.AutoModelForCausalLM.from_pretrained(model_path) |
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model.to(device) |
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tokenizer = transformers.AutoTokenizer.from_pretrained( |
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model_path, |
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model_max_length=4096, |
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padding_side="right", |
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use_fast=False, |
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add_special_tokens=False, |
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) |
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inputs = tokenizer(inputs, return_tensors='pt') |
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inputs = inputs.to(device) |
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output_ids = model.generate(inputs["input_ids"], max_length=4096, do_sample=False, temperature=0.0, use_cache=True) |
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sequence_length = inputs["input_ids"].shape[1] |
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new_output_ids = output_ids[:, sequence_length:] |
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answers = tokenizer.batch_decode(new_output_ids, skip_special_tokens=True) |
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return answers |
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model_path = 'microsoft/Orca-2-13b' |
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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." |
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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." |
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# 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 |
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prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant" |
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answers = run_inference(model_path, prompt) |
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final_output = answers[0] if not should_filter_out(answers[0]) else "[Content Filtered]" |
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print(final_output) |
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``` |
