Manticore-13B-GGML

Maintainer: TheBloke

Total Score

66

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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Manticore-13B-GGML

Model overview

[object Object] is a large language model released by the OpenAccess AI Collective and maintained by TheBloke. It is a 13 billion parameter model trained on a diverse corpus of online data. TheBloke has provided a range of quantized versions of the model in the GGML format, allowing for efficient CPU and GPU inference using libraries like llama.cpp and text-generation-webui.

Model inputs and outputs

Inputs

  • The model takes raw text as input.

Outputs

  • The model generates coherent, fluent text outputs in response to the input.

Capabilities

Manticore-13B-GGML demonstrates strong natural language understanding and generation capabilities across a variety of tasks. It can be used for tasks like question answering, summarization, language translation, and open-ended text generation. The quantized GGML versions of the model enable efficient deployment on both CPU and GPU hardware.

What can I use it for?

The Manticore-13B-GGML model can be used for a wide range of natural language processing applications. Some potential use cases include:

  • Building chatbots and conversational agents
  • Generating creative content like stories, poems, or scripts
  • Automating content creation for blogs, social media, or marketing
  • Powering virtual assistants with natural language understanding

Things to try

One interesting aspect of the Manticore-13B-GGML model is the variety of quantization methods available, which allow for different tradeoffs between model size, inference speed, and quality. Experimenting with the different quantized versions could be a good way to find the right balance for your specific use case and hardware setup.



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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