OpenOrca-Platypus2-13B-GGML
Maintainer: TheBloke
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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 OpenOrca-Platypus2-13B-GGML is a large language model created by Open-Orca. It is an open-source model that has been trained on explain-tuned datasets, including the WizardLM, Alpaca, and Dolly-V2 datasets. The model has been optimized for reasoning tasks and is designed to excel at understanding the thought process behind answers.
The model is available in a range of quantized formats, including GPTQ and GGML, which allow for efficient inference on both CPUs and GPUs. These files were generously provided by TheBloke, who has also made quantized versions of similar models like the orca_mini_13B-GGML and orca_mini_3B-GGML available.
Model inputs and outputs
The OpenOrca-Platypus2-13B-GGML model is a text-to-text model, meaning it takes text as input and generates text as output. The model can be used for a variety of language tasks, such as question answering, summarization, and open-ended generation.
Inputs
- Prompts: The model takes natural language prompts as input, which can include instructions, questions, or other text.
Outputs
- Text generation: The model generates relevant and coherent text in response to the input prompts.
Capabilities
The OpenOrca-Platypus2-13B-GGML model has been designed to excel at reasoning tasks, with the goal of understanding and replicating the thought process behind answers. It has been trained on a diverse range of datasets, which allows it to handle a variety of language tasks with high accuracy.
What can I use it for?
The OpenOrca-Platypus2-13B-GGML model can be used for a wide range of applications, such as:
- Question answering: The model can be used to answer questions on a variety of topics, drawing upon its broad knowledge base.
- Summarization: The model can be used to generate concise summaries of longer text, capturing the key points and ideas.
- Open-ended generation: The model can be used to generate creative, coherent text on a wide range of topics, making it useful for tasks like story writing or content creation.
Things to try
One interesting aspect of the OpenOrca-Platypus2-13B-GGML model is its focus on replicating the thought process behind answers. Users could try providing the model with prompts that require reasoning or explanation, and then analyze the generated responses to better understand how the model approaches these types of tasks.
Additionally, users could experiment with different quantization levels to find the right balance between model performance and resource requirements for their specific use case. The range of quantized models provided by TheBloke offer a variety of options to choose from.
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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