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README.md
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<!-- Provide a quick summary of what the model is/does. -->
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**slim-ner** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.
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slim-ner has been fine-tuned for **named entity extraction** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
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SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow.
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Each slim model has a 'quantized tool' version, e.g., [**'slim-ner-tool'**](https://huggingface.co/llmware/slim-ner-tool).
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<!-- Provide a quick summary of what the model is/does. -->
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**slim-ner** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of 1b parameter small, specialized decoder-based models, fine-tuned for function-calling.
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slim-ner has been fine-tuned for **named entity extraction** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
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SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow.
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SLIM models can be used 'out of the box' for rapid prototyping in most general purpose use cases, and are designed to serve as a solid base that can be easily fine-tuned and adapted for specialized production use cases.
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Each slim model has a 'quantized tool' version, e.g., [**'slim-ner-tool'**](https://huggingface.co/llmware/slim-ner-tool).
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