Orca-2-13b
Maintainer: microsoft
651
🏷️
| Property | Value |
|---|---|
| Model Link | View 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-2-13b is a research model developed by Microsoft that aims to enhance the reasoning capabilities of small language models. It is a fine-tuned version of the LLAMA-2 base model, trained on a synthetic dataset created to improve its reasoning abilities. The model is not optimized for chatting and is best used after being fine-tuned for a specific task or after further training with RLHF or DPO.
Similar models include StableBeluga2, which is a LLAMA2 70B model fine-tuned on an Orca-style dataset, and llama2-13b-orca-8k-3319, which is a fine-tuning of the LLAMA-2 13B model with an 8K context size on a long-conversation variant of the Dolphin dataset.
Model inputs and outputs
Orca-2-13b is designed for research purposes and provides single-turn responses in tasks such as reasoning over user-given data, reading comprehension, math problem-solving, and text summarization. The model is particularly focused on enhancing reasoning capabilities.
Inputs
- User-provided data or instructions for the model to reason about and respond to
Outputs
- Single-turn responses from the model, demonstrating its reasoning and problem-solving abilities
Capabilities
Orca-2-13b is focused on improving the reasoning capabilities of small language models. It has been evaluated on a wide range of tasks, including BigBench-Hard and AGIEval, and has shown significant improvements over its base LLAMA-2 model.
What can I use it for?
Orca-2-13b is intended for research purposes, to allow the research community to assess its abilities and provide a foundation for building better frontier models. The model could be useful for researchers and developers working on enhancing the reasoning capabilities of language models, as well as for applications that require strong reasoning skills, such as question-answering, math problem-solving, or text summarization.
Things to try
Researchers and developers could explore fine-tuning Orca-2-13b on specific datasets or tasks to further improve its performance. They could also investigate the model's capabilities in different areas, such as multi-step reasoning, logical inference, or grounding in real-world knowledge.
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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