---
license: cc-by-sa-4.0
---

# SLIM-BOOLEAN-TOOL

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


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

This is an experimental model that takes as input a context passage, a yes-no question, and an optional (explain) parameter, and generates a response consisting of a python dictionary with two keys- 'answer' consisting of the 'yes/no' classification, and 'explanation' which provides a text explanation, derived from the source passage that explains the boolean classification assesment.  All of the details on the prompt template as provided in the config.json file in this model repo, along with several examples.  


[**slim-boolean**](https://huggingface.co/llmware/slim-boolean) 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.

To pull the model via API:  

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


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