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  <!-- Provide a quick summary of what the model is/does. -->
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- **slim-summary-tool** is a 4_K_M quantized GGUF version of slim-summary, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
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- The size of the self-contained GGUF model binary is 1.71 GB, which is small enough to run locally on a CPU with reasonable inference speed.
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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**](https://huggingface.co/llmware/slim-summary) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.
 
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  To pull the model via API:
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  <!-- Provide a quick summary of what the model is/does. -->
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+ **slim-summary-tool** is a 4_K_M quantized GGUF version of slim-summary, providing a small, fast inference implementation, optimized for multi-model concurrent deployment, to provide high-quality summarizations of complex business documents, on a small, specialized locally-deployable model.
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+ The size of the self-contained GGUF model binary is 1.71 GB, which is small enough to run locally on a CPU with reasonable inference speed, and has been optimized to maximize high-quality with the ability to deploy 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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+ Please see the usage notes at: [**slim-summary**](https://huggingface.co/llmware/slim-summary)
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  To pull the model via API:
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