Orca-2-7b
| Property | Value |
|---|---|
| Author | Microsoft |
| License | Microsoft Research License |
| Paper | View Paper |
| Base Model | LLaMA-2 |
What is Orca-2-7b?
Orca-2-7b is a specialized research language model developed by Microsoft, built upon the LLaMA-2 architecture. This model is specifically designed to excel in reasoning tasks and provides sophisticated single-turn responses in areas such as data analysis, reading comprehension, mathematical problem-solving, and text summarization.
Implementation Details
The model is implemented using PyTorch and leverages the Transformers architecture. It's trained on a carefully curated synthetic dataset, processed through Microsoft Azure content filters to ensure quality and safety. The model utilizes special tokens for conversation formatting and supports both CPU and GPU inference.
- Built on LLaMA-2 architecture with 7B parameters
- Implements specialized reasoning capabilities through synthetic data training
- Supports content safety integration with Azure AI Content Safety
- Uses markup-based conversation formatting with special tokens
Core Capabilities
- Advanced reasoning over user-provided data
- Reading comprehension and analysis
- Mathematical problem solving
- Text summarization
- Zero-shot learning performance
Frequently Asked Questions
Q: What makes this model unique?
Orca-2-7b stands out for its focused development on reasoning capabilities in a smaller model size, demonstrating that smaller language models can be enhanced for specific capabilities through carefully designed synthetic training data.
Q: What are the recommended use cases?
The model is intended strictly for research purposes and is particularly well-suited for studying reasoning capabilities in language models. It performs best in single-turn interactions involving analysis, comprehension, and problem-solving tasks, though it requires fine-tuning for chat applications.
