orca_mini_13B-GGML

Maintainer: TheBloke

Total Score

56

Last updated 5/28/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The orca_mini_13B-GGML is a 13 billion parameter AI model created by Pankaj Mathur. It is based on the OpenLLaMA architecture and was trained on a custom dataset combining the WizardLM, Alpaca, and Dolly-V2 datasets. The model was further tuned using techniques from the Orca Research Paper to instill more thoughtful and explanatory behavior.

The model is available in GGML format, which allows for efficient CPU and GPU-accelerated inference using tools like llama.cpp, text-generation-webui, and KoboldCpp. This makes it accessible for a wide range of users and use cases.

Model inputs and outputs

Inputs

  • Prompts: The model takes in natural language prompts as input, which can range from simple instructions to more complex scenarios.

Outputs

  • Text generation: The model generates coherent, human-like text as output, with the ability to continue and expand upon the given prompt.

Capabilities

The orca_mini_13B-GGML model demonstrates strong performance on a variety of language tasks, including open-ended generation, question answering, and task-oriented dialogue. It is particularly adept at providing detailed, thoughtful responses that showcase its understanding of the prompt and ability to generate relevant, explanatory text.

What can I use it for?

The orca_mini_13B-GGML model's capabilities make it well-suited for a wide range of applications, such as creative writing assistants, chatbots, and knowledge-sharing platforms. Developers could leverage the model to build applications that generate engaging, informative content or assist users with a variety of tasks.

Things to try

One key feature of the orca_mini_13B-GGML model is its ability to provide detailed, step-by-step explanations in response to prompts. Developers could experiment with prompts that ask the model to break down complex topics or walk through multi-step processes, and observe the model's ability to generate coherent, educational responses.



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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