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license: cc-by-sa-4.0 |
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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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{'sentiment': ['positive'], people': ['..'], 'organization': ['..'], |
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'place': ['..]} |
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This 3B parameter 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder. |
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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, providing an intuitive, flexible natural language response, without complex prompting, and with improved generalization and ability to fine-tune to a specific domain use case. |
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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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Darren Oberst & llmware team |
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[Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h) |
