transformers gguf stablelm license:cc-by-sa-4.0 endpoints_compatible region:us
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license: cc-by-sa-4.0
---

# SLIM-SA_NER-TOOL

<!-- Provide a quick summary of what the model is/does. -->


**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.  

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.:

    {'sentiment': ['positive'], people': ['..'], 'organization': ['..'],
     'place': ['..]}

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.

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.



To pull the model via API:  

    from huggingface_hub import snapshot_download           
    snapshot_download("llmware/slim-sa-ner-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  
    

Load in your favorite GGUF inference engine, or try with llmware as follows:

    from llmware.models import ModelCatalog  
    
    # to load the model and make a basic inference
    model = ModelCatalog().load_model("slim-sa-ner-tool")
    response = model.function_call(text_sample)  

    # this one line will download the model and run a series of tests
    ModelCatalog().tool_test_run("slim-sa-ner-tool", verbose=True)  


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.  


## Model Card Contact

Darren Oberst & llmware team  

[Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)