orca_mini_3b
Maintainer: pankajmathur
157
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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
The orca_mini_3b model is an OpenLLaMa-3B model trained on a mix of datasets including WizardLM, Alpaca, and Dolly-V2. It applies the dataset construction approaches from the Orca Research Paper to create an "explain tuned" model designed to learn the thought process from the ChatGPT teacher model.
Model inputs and outputs
Inputs
- System prompt: A short prompt provided at the start of the interaction that sets the context and instructions for the model.
- User instruction: The specific task or query that the user wants the model to address.
- User input (optional): Additional context or information provided by the user to help the model respond.
Outputs
- Model response: The generated text from the model addressing the user's instruction. The model aims to provide a well-reasoned and helpful response.
Capabilities
The orca_mini_3b model is capable of engaging in a wide variety of text-to-text tasks, such as question answering, task completion, and open-ended conversation. It demonstrates strong reasoning and explanatory capabilities, drawing insights from its training data to provide thoughtful and substantive responses.
What can I use it for?
The orca_mini_3b model could be useful for applications that require natural language understanding and generation, such as chatbots, virtual assistants, and content creation tools. Its ability to learn the thought process from ChatGPT makes it well-suited for tasks that benefit from clear, step-by-step explanations.
Things to try
One interesting aspect of the orca_mini_3b model is its use of a "system prompt" to set the context and instructions for the interaction. Experimenting with different system prompts could yield insights into how the model's responses change based on the framing and guidance provided upfront. Additionally, prompting the model with open-ended questions or tasks that require reasoning and analysis could reveal its strengths in those areas.
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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