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license: cc-by-sa-4.0
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# SLIM-XSUM-TOOL

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


**slim-xsum-tool** is a 4_K_M quantized GGUF version of slim-xsum, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  

This model implements an 'extreme summarization' (e.g., 'xsum') function based on the parameter key "xsum" that generates an LLM text output in the form of a python dictionary as follows:  

     `{'xsum': ['Stock Market declines on worries of interest rates.']} `  
     
The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs through the use of function-calling and small specialized LLMs.  

[**slim-xsum**](https://huggingface.co/llmware/slim-xsum) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.


To pull the model via API:  

    from huggingface_hub import snapshot_download           
    snapshot_download("llmware/slim-xsum-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-xsum-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-xsum-tool", verbose=True)  


Note: please review [**config.json**](https://huggingface.co/llmware/slim-xsum-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)