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**slim-boolean-tool** is a 4_K_M quantized GGUF version of slim-boolean, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
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This is an experimental model that takes as input a context passage, a yes-no question, and an optional (explain) parameter, and generates a response consisting of a python dictionary with two keys- 'answer' consisting of the 'yes/no' classification, and 'explanation' which provides a text explanation, derived from the source passage that explains the boolean classification assesment. All of the details on the prompt template
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[**slim-boolean**](https://huggingface.co/llmware/slim-boolean) 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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**slim-boolean-tool** is a 4_K_M quantized GGUF version of slim-boolean, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
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This is an experimental model that takes as input a context passage, a yes-no question, and an optional (explain) parameter, and generates a response consisting of a python dictionary with two keys- 'answer' consisting of the 'yes/no' classification, and 'explanation' which provides a text explanation, derived from the source passage that explains the boolean classification assesment. All of the details on the prompt template are provided in the config.json file in this model repo, along with several examples.
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If you are interested in fine-tuning this model for a specific domain, please see: [**slim-boolean**](https://huggingface.co/llmware/slim-boolean).
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To pull the model via API:
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