openorca-platypus2-13b
Maintainer: niron1
1
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | View on Arxiv |
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Model overview
openorca-platypus2-13b is a powerful language model that combines the capabilities of two prominent models - [object Object] and [object Object]. Developed by the team at Open-Orca, this merged model builds on the strengths of its predecessors to deliver impressive performance across a range of benchmarks.
Similar models include the Mistral-7B-v0.1 fine-tuned by nateraw and the OpenOrca Platypus2 13B - GGML quantized by TheBloke. These models showcase the versatility and potential of the OpenOrca and Platypus frameworks.
Model inputs and outputs
openorca-platypus2-13b is an autoregressive language model that takes in a text prompt and generates a response. The key input parameters include:
Inputs
- prompt: The text prompt to be used as input for the model.
- temperature: A parameter that controls the randomness of the generated output, with higher values leading to more diverse but potentially less coherent responses.
- max_new_tokens: The maximum number of new tokens the model will generate in response to the input prompt.
- repetition_penalty: A parameter that penalizes the model for repeating the same words or phrases, encouraging more diverse output.
- seed: A random number seed used to ensure reproducibility of the model's outputs.
Outputs
- generated text: The model's response, which can be a continuation of the input prompt or a completely new passage of text.
Capabilities
The openorca-platypus2-13b model has demonstrated impressive performance on a variety of benchmarks, including the Hendricks MMLU (5-shot) test with a score of 59.5, the ARC (25-shot) test with a score of 62.88, and the HellaSwag (10-shot) test with a score of 83.19. Additionally, the model scored 52.69 on the TruthfulQA (0-shot) test.
The model also exhibits strong performance on the AGIEval and BigBench-Hard evaluations, outperforming its base OpenOrcaxOpenChat-Preview2-13B model by 12% and 5% respectively.
What can I use it for?
The openorca-platypus2-13b model can be used for a variety of natural language processing tasks, such as:
- Content Generation: The model can be used to generate coherent and relevant text, making it useful for tasks like article writing, story generation, and creative writing.
- Question Answering: With its strong performance on benchmarks like MMLU and TruthfulQA, the model can be used to answer a wide range of questions across various domains.
- Summarization: The model's ability to generate concise and informative text could be leveraged for summarizing long-form content.
- Dialogue Systems: The model's conversational capabilities make it a promising candidate for building chatbots and virtual assistants.
Things to try
One interesting aspect of the openorca-platypus2-13b model is its ability to handle instructions and engage in task-oriented dialogue. Try prompting the model with open-ended instructions or requests and observe the range and quality of its responses. Additionally, the model's strong performance on logical reasoning and STEM-focused tasks suggests it could be a valuable tool for scientific and technical applications.
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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