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license: cc-by-sa-4.0 |
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# SLIM-SUMMARY-TOOL |
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<!-- Provide a quick summary of what the model is/does. --> |
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**slim-summary-tool** is a 4_K_M quantized GGUF version of slim-summary, 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 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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Please see the usage notes at: [**slim-summary**](https://huggingface.co/llmware/slim-summary) |
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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-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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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-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-summary-tool", verbose=True) |
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Note: please review [**config.json**](https://huggingface.co/llmware/slim-summary-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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Darren Oberst & llmware team |
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[Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h) |
