wizard-mega-13b
Maintainer: openaccess-ai-collective
105
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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 wizard-mega-13b model, also known as the Manticore 13B model, is a large language model developed by the OpenAccess AI Collective. It is a fine-tuned version of the LlaMa 13B model, trained on datasets such as ShareGPT, WizardLM, and Wizard-Vicuna. These datasets have been filtered to remove responses where the model indicates it is an AI language model or declines to respond.
The Manticore 13B model has also been updated and fine-tuned on additional datasets, including a subset of Alpaca-CoT for roleplay and chain-of-thought prompts, GPT4-LLM-Cleaned, GPTeacher-General-Instruct, and various subsets of the MMLU dataset for specific subjects. This additional fine-tuning has resulted in the Manticore 13B model, which aims to provide more helpful, detailed, and polite responses compared to the original Wizard Mega 13B model.
Model inputs and outputs
Inputs
- Free-form text prompts that the model uses to generate a response.
Outputs
- Generated text responses, which can range from short, concise answers to longer, more detailed responses depending on the prompt.
Capabilities
The wizard-mega-13b model, or Manticore 13B, is capable of generating coherent and contextually appropriate text across a wide range of topics. It can be used for tasks such as question answering, summarization, language generation, and task completion. The model's fine-tuning on datasets like ShareGPT, WizardLM, and Wizard-Vicuna has equipped it with the ability to provide more helpful, detailed, and polite responses compared to the original Wizard Mega 13B model.
What can I use it for?
The Manticore 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 on a wide range of topics, providing detailed and informative responses.
- Summarization: The model can be used to summarize longer text passages into concise, high-level summaries.
- Language Generation: The model can be used to generate coherent and contextually appropriate text, such as stories, articles, or dialogues.
- Task Completion: The model can be used to assist with task-oriented activities, such as writing code, solving math problems, or providing step-by-step instructions.
The Hugging Face Spaces demo allows you to try out the Manticore 13B model and see its capabilities in action.
Things to try
Some interesting things to try with the Manticore 13B model include:
- Experimenting with different types of prompts, such as open-ended questions, specific task instructions, or creative writing prompts, to see the range of responses the model can generate.
- Evaluating the model's ability to provide detailed and helpful answers to questions on a variety of subjects, from science and history to current events and popular culture.
- Assessing the model's coherence and logical reasoning skills by asking it to break down complex problems or provide step-by-step solutions to tasks.
- Exploring the model's potential for creative writing or storytelling by giving it open-ended prompts and seeing the unique narratives it can generate.
By trying out these and other use cases, you can gain a better understanding of the Manticore 13B model's capabilities and find ways to integrate it into your own projects or workflows.
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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