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README.md
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<!-- Provide a quick summary of what the model is/does. -->
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**slim-qa-gen-
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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:
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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).
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slim-qa-gen-
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[**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.
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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-qa-gen-
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Load in your favorite GGUF inference engine, or try with llmware as follows:
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# to load the model and make a basic inference
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model = ModelCatalog().load_model("slim-qa-gen-tiny-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-qa-gen-
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Note: please review [**config.json**](https://huggingface.co/llmware/slim-
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## Model Card Contact
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<!-- Provide a quick summary of what the model is/does. -->
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**slim-qa-gen-phi-3-tool** is a 4_K_M quantized GGUF version of slim-qa-gen-phi-3, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
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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:
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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).
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[**slim-qa-gen-phi-3*](https://huggingface.co/llmware/slim-qa-gen-phi-3) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
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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-qa-gen-phi-3-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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# to load the model and make a basic inference
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model = ModelCatalog().load_model("slim-qa-gen-tiny-tool")
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response = model.function_call(text_sample, params=["boolean"])
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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-qa-gen-phi-3-tool", verbose=True)
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Note: please review [**config.json**](https://huggingface.co/llmware/slim-qa-gen-phi-3-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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