Create README.md

README.md

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+---
+pipeline_tag: text-generation
+---
+
+# Orca 2
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+In Orca 2, we continue exploring how improved training signals can give smaller LMs enhanced reasoning abilities, typically
+found only in much larger models. We seek to teach small LMs to employ different solution
+strategies for different tasks, potentially different from the one used by the
+larger model. For example, while larger models might provide a direct answer
+to a complex task, smaller models may not have the same capacity. In Orca
+2, we teach the model various reasoning techniques (step-by-step, recall
+then generate, recall-reason-generate, direct answer, etc.). More crucially,
+we aim to help the model learn to determine the most effective solution
+strategy for each task. Orca 2 models were trained by continual training of LLaMA-2 base models of the same size.
+
+
+## Model Details
+
+Refer to LLaMA-2 for details on model architectures.
+
+## Uses
+
+
+## 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 including by its training
+process, including:
+
+**Data Biases**: Large language models, trained on extensive data, can inadvertently carry
+biases present in the source data. Consequently, the models may generate outputs that could
+be potentially biased or unfair.
+
+**Lack of Contextual Understanding**: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting
+in potential inaccuracies or nonsensical responses.
+
+**Lack of Transparency**: Due to the complexity and size, large language models can act
+as “black boxes”, making it difficult to comprehend the rationale behind specific outputs or
+decisions. We recommend reviewing transparency notes from Azure for more information.
+
+**Content Harms**: There are various types of content harms that large language models
+can cause. It is important to be aware of them when using these models, and to take
+actions to prevent them. It is recommended to leverage various content moderation services
+provided by different companies and institutions. On an important note, we hope for better
+regulations and standards from government and technology leaders around content harms
+for AI technologies in future. We value and acknowledge the important role that research
+and open source community can play in this direction.
+
+**Hallucination**: It is important to be aware and cautious not to entirely rely on a given
+language model for critical decisions or information that might have deep impact as it is
+not obvious how to prevent these models from fabricating content. Moreover, it is not clear
+whether small models may be more susceptible to hallucination in ungrounded generation
+use cases due to their smaller sizes and hence reduced memorization capacities. This is an
+active research topic and we hope there will be more rigorous measurement, understanding
+and mitigations around this topic.
+
+**Potential for Misuse**: Without suitable safeguards, there is a risk that these models could
+be maliciously used for generating disinformation or harmful content.
+
+**Data Distribution**: Orca 2’s performance is likely to correlate strongly with the distribution
+of the tuning data. This correlation might limit its accuracy in areas underrepresented in
+the training dataset such as math, coding, and reasoning.
+
+**System messages**: Orca 2 demonstrates variance in performance depending on the system
+instructions. Additionally, the stochasticity introduced by the model size may lead to
+generation of non-deterministic responses to different system instructions.
+
+**Zero-Shot Settings**: Orca 2 was trained on data that mostly simulate zero-shot settings.
+While the model demonstrate very strong performance in zero-shot settings, it does not show
+the same gains of using few-shot learning compared to other, specially larger, models.
+
+**Synthetic data**: As Orca 2 is trained on synthetic data, it could inherit both the advantages
+and shortcomings of the models and methods used for data generation. We posit that Orca
+2 benefits from the safety measures incorporated during training and safety guardrails (e.g.,
+content filter) within the Azure OpenAI API. However, detailed studies are required for
+better quantification of such risks.
+
+This model is solely designed for research settings, and its testing has only been carried
+out in such environments. It should not be used in downstream applications, as additional
+analysis is needed to assess potential harm or bias in the proposed application.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+