Maintainer: openaccess-ai-collective

Total Score


Last updated 5/17/2024


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

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Model overview

manticore-13b is a large language model fine-tuned by the OpenAccess AI Collective on a range of datasets including ShareGPT, WizardLM, and Wizard-Vicuna. It is a larger, more capable model compared to similar open-source models like Llama 2-13B and Nous-Hermes-Llama2-13b, with demonstrated strong performance on a range of benchmarks.

Model inputs and outputs

manticore-13b is a text-to-text model, taking in natural language prompts as input and generating relevant, coherent text responses as output. The model can handle a wide variety of prompts, from open-ended questions to detailed instructions.


  • Natural language prompts of varying length, from single sentences to multi-paragraph text
  • Prompts can cover a broad range of topics, from creative writing to analysis and problem-solving


  • Coherent, relevant text responses generated to address the input prompts
  • Responses can range from short, concise answers to detailed, multi-paragraph outputs


The manticore-13b model demonstrates strong capabilities across many domains, including question answering, task completion, and open-ended generation. It is able to draw upon its broad knowledge base to provide informative and insightful responses, and can also engage in more creative and speculative tasks.

What can I use it for?

manticore-13b can be a powerful tool for a variety of applications, such as:

  • Content generation: Generating original text content, such as articles, stories, or scripts
  • Dialogue systems: Building chatbots and virtual assistants that can engage in natural conversations
  • Question answering: Providing detailed and accurate answers to a wide range of questions
  • Task completion: Following complex instructions to complete tasks like research, analysis, or problem-solving

The model's versatility and strong performance make it a valuable resource for researchers, developers, and businesses looking to leverage large language models for their projects.

Things to try

One interesting aspect of manticore-13b is its ability to engage in more open-ended and speculative tasks, such as creative writing or thought experiments. Try prompting the model with ideas or scenarios and see how it responds, exploring the boundaries of its capabilities. You might be surprised by the novel and insightful suggestions it can generate.

Another interesting area to explore is the model's performance on specialized or technical tasks, such as programming, data analysis, or scientific reasoning. While it is a general-purpose language model, manticore-13b may be able to provide valuable assistance in these domains as well.

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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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.

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