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**slim-summary-tiny-tool** is a 4_K_M quantized GGUF version of slim-summary-tiny, providing a small, fast inference implementation, to provide high-quality summarizations of complex business documents, on a small, specialized locally-deployable model with summary output structured as a python list of key points.
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The size of the self-contained GGUF model binary is ~700 MB, which is small enough to run locally on a CPU with reasonable inference speed, and has been designed to balance
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The model takes as input a text passage, an optional parameter with a focusing phrase or query, and an experimental optional (N) parameter, which is used to guide the model to a specific number of items return in a summary list.
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**slim-summary-tiny-tool** is a 4_K_M quantized GGUF version of slim-summary-tiny, providing a small, fast inference implementation, to provide high-quality summarizations of complex business documents, on a small, specialized locally-deployable model with summary output structured as a python list of key points.
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The size of the self-contained GGUF model binary is ~700 MB, which is small enough to run locally on a CPU with reasonable inference speed, and has been designed to balance solid quality with fast loading and inference on a local machine.
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The model takes as input a text passage, an optional parameter with a focusing phrase or query, and an experimental optional (N) parameter, which is used to guide the model to a specific number of items return in a summary list.
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