microsoft/Orca-2-7b

Orca 2: Enhancing Reasoning in Small Language Models

Orca 2 is a 7 billion parameter research model developed by Microsoft, fine-tuned from the LLaMA-2 base. Its primary goal is to demonstrate that capable models and complex workflows can generate synthetic data to teach Small Language Models (SLMs) advanced capabilities, particularly reasoning.

Key Capabilities

• Enhanced Reasoning: Specifically trained to excel in reasoning tasks, including interpreting user data, reading comprehension, and mathematical problem-solving.
• Synthetic Data Training: Utilizes a synthetic dataset, created with advanced prompts and multiple calls from larger models, to impart new capabilities to SLMs.
• Research Focus: Intended for research purposes to assess SLM abilities and provide a foundation for building more capable frontier models.
• LLaMA-2 Foundation: Inherits the core architecture and general capabilities of its LLaMA-2 base, while focusing on improving reasoning.

Important Considerations

• Research Model Only: Not optimized for general chat or production applications and has not undergone RLHF or DPO training.
• Limitations: Shares limitations with its LLaMA-2 base, including potential data biases, lack of deep contextual understanding, and susceptibility to hallucination.
• System Message Sensitivity: Performance can vary based on system instructions, and it may not show the same few-shot learning gains as larger models.

Orca 2's weights are publicly available to support further research into the development, evaluation, and alignment of SLMs, particularly in the domain of reasoning.