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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**](https://huggingface.co/llmware/slim-sa-ner), providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
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  slim-sa-ner combines two of the most popular traditional classifier functions (Sentiment Analysis and Named Entity Recognition), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.:
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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
 
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+ license: apache-2.0
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+ inference: false
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+ # SLIM-SA_NER-PHI-3-GGUF
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  <!-- Provide a quick summary of what the model is/does. -->
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+ **slim-sa-ner-phi-3-gguf** is a 4_K_M quantized GGUF version of [**slim-sa-ner**](https://huggingface.co/llmware/slim-sa-ner), providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
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  slim-sa-ner combines two of the most popular traditional classifier functions (Sentiment Analysis and Named Entity Recognition), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.:
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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-phi-3-gguf", 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-phi-3-gguf")
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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-phi-3-gguf", verbose=True)
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+ Note: please review [**config.json**](https://huggingface.co/llmware/slim-sa-ner-phi-3-gguf/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