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  license: cc-by-sa-4.0
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  ---
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- # SLIM-EXTRACT-TOOL
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  <!-- Provide a quick summary of what the model is/does. -->
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- **slim-extract-tool** is a 4_K_M quantized GGUF version of slim-extract, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
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- [**slim-extract**](https://huggingface.co/llmware/slim-extract) 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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  from huggingface_hub import snapshot_download
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- snapshot_download("llmware/slim-extract-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
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  Load in your favorite GGUF inference engine, or try with llmware as follows:
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  from llmware.models import ModelCatalog
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  # to load the model and make a basic inference
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- model = ModelCatalog().load_model("slim-extract-tool")
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  response = model.function_call(text_sample)
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  # this one line will download the model and run a series of tests
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- ModelCatalog().tool_test_run("slim-extract-tool", verbose=True)
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- Note: please review [**config.json**](https://huggingface.co/llmware/slim-extract-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
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  ## Model Card Contact
 
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  license: cc-by-sa-4.0
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  ---
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+ # SLIM-SA_NER-TOOL
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  <!-- Provide a quick summary of what the model is/does. -->
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+ **slim-sa-ner-tool** is a 4_K_M quantized GGUF version of slim-sa-ner, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
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+ [**slim-sa-ner**](https://huggingface.co/llmware/slim-sa-ner) 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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  from huggingface_hub import snapshot_download
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+ snapshot_download("llmware/slim-sa-ner-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
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  Load in your favorite GGUF inference engine, or try with llmware as follows:
 
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  from llmware.models import ModelCatalog
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  # to load the model and make a basic inference
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+ model = ModelCatalog().load_model("slim-sa-ner-tool")
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  response = model.function_call(text_sample)
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  # this one line will download the model and run a series of tests
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+ ModelCatalog().tool_test_run("slim-sa-ner-tool", verbose=True)
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+ Note: please review [**config.json**](https://huggingface.co/llmware/slim-sa-ner-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
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  ## Model Card Contact