orca_mini_13b
Maintainer: pankajmathur
98
👨🏫
| 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_mini_13b is an OpenLLaMa-13B model fine-tuned on explain-tuned datasets. The dataset was created using instructions and input from WizardLM, Alpaca, and Dolly-V2 datasets, applying approaches from the Orca Research Paper. This helps the model learn the thought process from the teacher model, which is the GPT-3.5-turbo-0301 version of ChatGPT.
Model inputs and outputs
The orca_mini_13b model takes a combination of system prompts and user instructions as input, and generates relevant text responses as output.
Inputs
- System prompt: A prompt that sets the context for the model, describing the role and goals of the AI assistant.
- User instruction: The task or query that the user wants the model to address.
- Input (optional): Additional context or information that the user provides to help the model complete the task.
Outputs
- Response: The model's generated text response to the user's instruction, which aims to provide a detailed, thoughtful, and step-by-step explanation.
Capabilities
The orca_mini_13b model is capable of generating high-quality, explain-tuned responses to a variety of tasks and queries. It demonstrates strong performance on reasoning-based benchmarks like BigBench-Hard and AGIEval, indicating its ability to engage in complex, logical thinking.
What can I use it for?
The orca_mini_13b model can be used for a range of applications that require detailed, step-by-step explanations, such as:
- Educational or tutoring applications
- Technical support and customer service
- Research and analysis tasks
- General question-answering and information retrieval
By leveraging the model's explain-tuned capabilities, users can gain a deeper understanding of the topics and concepts being discussed.
Things to try
One interesting thing to try with the orca_mini_13b model is to provide it with prompts or instructions that require it to take on different expert roles, such as a logician, mathematician, or physicist. This can help uncover the model's breadth of knowledge and its ability to tailor its responses to the specific needs of the task at hand.
Another interesting approach is to explore the model's performance on open-ended, creative tasks, such as generating poetry or short stories. The model's strong grounding in language and reasoning may translate into an ability to produce engaging and insightful creative output.
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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