orca_mini_v3_7b
Maintainer: pankajmathur
40
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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
orca_mini_v3_7b is a 7 billion parameter language model trained by Pankaj Mathur using an OpenLLaMA base and fine-tuned on datasets from WizardLM, Alpaca, and Dolly-V2. The model was trained using approaches from the Orca Research Paper to learn the "thought process" of the ChatGPT model. This allows the model to provide more coherent and context-aware responses compared to vanilla instruction tuning. Similar models include the orca_mini_3b and orca_mini_13b, which are 3 billion and 13 billion parameter versions respectively.
Model inputs and outputs
orca_mini_v3_7b is a text-to-text model that can take natural language prompts as input and generate relevant text responses. The prompts typically include a "system" description that sets the context for the assistant, followed by a user instruction or query.
Inputs
- System description: Provides context for the assistant, such as "You are an AI assistant that follows instructions extremely well. Help as much as you can."
- User instruction/query: The natural language prompt or request for the assistant to respond to.
- Optional input: Some prompts may include additional input data, such as a specific topic or background information.
Outputs
- Generated text response: The model's generated text response to the user's instruction or query, based on the provided context.
Capabilities
The orca_mini_v3_7b model can be used for a variety of natural language processing tasks, such as question answering, dialogue, summarization, and creative writing. It has shown strong performance on benchmark tasks like ARC Challenge, HellaSwag, and MMLU. The model's ability to learn the "thought process" of ChatGPT allows it to provide more coherent and context-aware responses compared to vanilla instruction-tuned models.
What can I use it for?
The orca_mini_v3_7b model can be used for a wide range of applications that require natural language understanding and generation, such as virtual assistants, chatbots, content creation tools, and educational applications. For example, you could use it to build a chatbot that can engage in open-ended conversations, answer questions, or help with task planning and creative writing. You could also fine-tune the model further on specific datasets or tasks to adapt it to your particular use case.
Things to try
Some interesting things to try with the orca_mini_v3_7b model include:
- Prompting the model with complex, multi-step instructions or queries to see how it handles long-form reasoning and task-completion.
- Exploring the model's ability to engage in open-ended dialogue by providing a range of conversational prompts and observing the flow and coherence of the responses.
- Experimenting with different prompting techniques, such as using system instructions to guide the model's tone, personality, or knowledge domain.
- Fine-tuning the model on your own datasets or tasks to see how it can be adapted to specific use cases.
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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