Orca-2-7b
Maintainer: microsoft
206
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
Orca-2-7b is a large language model created by Microsoft for research purposes. It is a fine-tuned version of the LLaMA-2 base model, with a focus on enhancing the model's reasoning abilities. The training data for Orca-2-7b was a synthetic dataset designed to improve the reasoning capabilities of smaller language models.
Model inputs and outputs
Orca-2-7b is designed to provide single-turn responses for tasks such as reasoning over user-provided data, reading comprehension, math problem-solving, and text summarization. The model is particularly focused on excelling in reasoning-related tasks.
Inputs
- User-provided data, prompts, or instructions for the model to reason about or respond to
Outputs
- Single-turn textual responses from the model, based on the provided inputs
Capabilities
Orca-2-7b is designed to be a research model, showcasing that capable models and complex workflows can be used to create synthetic data that can teach smaller language models new capabilities, such as reasoning. The model inherits capabilities and limitations from its LLaMA-2 base, but the additional training on the synthetic dataset is intended to enhance its reasoning abilities.
What can I use it for?
Orca-2-7b is intended for research purposes, allowing the research community to assess its abilities and use it as a foundation for building better frontier models. The model is not optimized for chatbot use cases and has not been trained with RLHF or DPO, so it is best used after being fine-tuned for a specific task or chat application.
Things to try
Researchers and developers can use Orca-2-7b to explore new approaches to enhancing the reasoning capabilities of language models, either by fine-tuning the model on additional datasets or by using it as a starting point for further model development and research. The model's performance on reasoning-focused benchmarks and tasks can also be investigated to better understand its strengths and limitations.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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