OpenOrca-Preview1-13B

Maintainer: Open-Orca

Total Score

148

Last updated 5/27/2024

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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

OpenOrca-Preview1-13B is a large language model developed by the Open-Orca team. It was fine-tuned using the team's own OpenOrca dataset, which aims to reproduce the dataset from Microsoft Research's Orca Paper. The model was trained on less than 6% of the full OpenOrca dataset, but still achieved strong performance on various benchmarks.

Similar models include the Mistral-7B-OpenOrca and the open_llama_13b models. The Mistral-7B-OpenOrca model is a further fine-tuned version of the Mistral 7B model using the OpenOrca dataset, while the open_llama_13b is an open-source reproduction of Meta's LLaMA model.

Model inputs and outputs

The OpenOrca-Preview1-13B model is a text-to-text transformer model, meaning it takes text as input and generates text as output. The model can be used for a variety of natural language processing tasks, such as question answering, language generation, and text summarization.

Inputs

  • Text prompts: The model can take in text prompts of varying lengths, which it uses to generate relevant and coherent responses.

Outputs

  • Generated text: The model outputs new text that is a continuation or response to the input prompt. The generated text can range from a single sentence to multiple paragraphs, depending on the task and prompt.

Capabilities

The OpenOrca-Preview1-13B model has shown strong performance on various benchmarks, including BigBench-Hard and AGIEval. It is able to perform well on hard reasoning tasks, with an average score of 0.3753 on BigBench-Hard and 0.3638 on AGIEval. This is around 60% of the improvement shown in the original Orca paper.

What can I use it for?

The OpenOrca-Preview1-13B model can be used for a variety of natural language processing tasks, such as:

  • Question Answering: The model can be used to answer questions based on the provided input prompt.
  • Language Generation: The model can be used to generate coherent and relevant text, such as for creative writing or dialogue generation.
  • Text Summarization: The model can be used to summarize longer passages of text into concise summaries.

You can try out the model in the Hugging Face Space provided by the Open-Orca team.

Things to try

One interesting aspect of the OpenOrca-Preview1-13B model is that it was trained on a filtered and curated subset of the full OpenOrca dataset, yet still achieved strong performance. This suggests that the team's data curation and preprocessing practices were effective in identifying high-quality training data.

You could experiment with the model by trying different types of prompts, from open-ended questions to more specific task-oriented queries. The team has also provided a Nomic Atlas map to visualize the full (pre-filtering) OpenOrca dataset, which could be an interesting resource to explore.



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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