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+ ---
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # Orca 2
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+ In Orca 2, we continue exploring how improved training signals can give smaller LMs enhanced reasoning abilities, typically
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+ found only in much larger models. We seek to teach small LMs to employ different solution
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+ strategies for different tasks, potentially different from the one used by the
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+ larger model. For example, while larger models might provide a direct answer
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+ to a complex task, smaller models may not have the same capacity. In Orca
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+ 2, we teach the model various reasoning techniques (step-by-step, recall
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+ then generate, recall-reason-generate, direct answer, etc.). More crucially,
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+ we aim to help the model learn to determine the most effective solution
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+ strategy for each task. Orca 2 models were trained by continual training of LLaMA-2 base models of the same size.
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+
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+
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+ ## Model Details
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+
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+ Refer to LLaMA-2 for details on model architectures.
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+
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+ ## Uses
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+
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+
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+ ## Bias, Risks, and Limitations
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]