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README.md
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license:
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# SLIM-SUMMARY-
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<!-- Provide a quick summary of what the model is/does. -->
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**slim-summary-
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The size of the self-contained GGUF model binary is 1.71 GB, which is small enough to run locally on a CPU with reasonable inference speed, and has been designed to balance high-quality with the ability to deploy on a local machine.
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The model takes as input a text passage, an optional parameter with a focusing phrase or query, and an experimental optional (N) parameter, which is used to guide the model to a specific number of items return in a summary list.
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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-summary-
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Load in your favorite GGUF inference engine, or try with llmware as follows:
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from llmware.models import ModelCatalog
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# to load the model and make a basic inference
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model = ModelCatalog().load_model("slim-summary-
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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-summary-
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Note: please review [**config.json**](https://huggingface.co/llmware/slim-summary-
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## Model Card Contact
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license: apache-2.0
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inference: false
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# SLIM-SUMMARY-PHI-3-GGUF
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<!-- Provide a quick summary of what the model is/does. -->
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**slim-summary-phi-3** is a finetune of phi-3 mini (3.8B parameters) to implement a function-calling summarization model, and then packaged as 4_K_M quantized GGUF, providing a small, fast inference implementation, to provide high-quality summarizations of complex business documents, on a small, specialized locally-deployable model with summary output structured as a python list of key points.
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The model takes as input a text passage, an optional parameter with a focusing phrase or query, and an experimental optional (N) parameter, which is used to guide the model to a specific number of items return in a summary list.
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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-summary-phi-3-gguf", 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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from llmware.models import ModelCatalog
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# to load the model and make a basic inference
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model = ModelCatalog().load_model("slim-summary-phi-3-gguf")
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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-summary-phi-3-gguf", verbose=True)
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Note: please review [**config.json**](https://huggingface.co/llmware/slim-summary-phi-3-gguf/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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