README.md

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license: apache-2.0
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# SLIM-QA-GEN-TINY-TOOL

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


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

This model implements a generative 'question' and 'answer' (e.g., 'qa-gen') function, which takes a context passage as an input, and then generates as an output a python dictionary consisting of two keys:  

     `{'question': ['What was the amount of revenue in the quarter?'], 'answer': ['$3.2 billion']} `  

The model has been designed to accept one of three different parameters to guide the type of question-answer created:  

  -- 'question, answer' (generates a standard question and answer),  
  -- 'boolean' (generates a 'yes-no' question and answer), and  
  -- 'multiple choice' (generates a multiple choice question and answer).  
  

slim-qa-gen-tiny-tool is a fine-tune of a tinyllama (1b) parameter model, designed for fast, local deployment and rapid testing and prototyping.   Please also see [slim-qa-gen-phi-3-tool](https://huggingface.co/llmware/slim-qa-gen-phi-3-tool), which is finetune of phi-3, and will provide higher-quality results, at the trade-off of slightly slower performance and requiring more memory.  


[**slim-qa-gen-tiny**](https://huggingface.co/llmware/slim-qa-gen-tiny) 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-qa-gen-tiny-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-qa-gen-tiny-tool", temperature=0.5, sample=True)
    response = model.function_call(text_sample)  

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


Note: please review [**config.json**](https://huggingface.co/llmware/slim-qa-gen-tiny-tool/blob/main/config.json) in the repository for prompt template 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)