Manticore-13B-GGML
Maintainer: TheBloke
66
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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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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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