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This model has been fine-tuned to implement a general-purpose extraction function that takes a custom key as input parameter, and generates a python dictionary consisting of that custom key with the value consisting of a list of the values associated with that key in the text.
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The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs.
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This model has been fine-tuned to implement a general-purpose extraction function that takes a custom key as input parameter, and generates a python dictionary consisting of that custom key with the value consisting of a list of the values associated with that key in the text.
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The size of the self-contained GGUF model binary is less than 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 intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs.
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