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| license: apache-2.0 |
| inference: false |
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| # SLIM-SUMMARY-TINY-TOOL |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| **slim-summary-tiny-tool** is a 4_K_M quantized GGUF version of slim-summary-tiny, 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 ~700 MB, which is small enough to run locally on a CPU with reasonable inference speed, and has been designed to balance solid quality with fast loading and inference 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-tiny**](https://huggingface.co/llmware/slim-summary-tiny) |
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| To pull the model via API: |
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| from huggingface_hub import snapshot_download |
| snapshot_download("llmware/slim-summary-tiny-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 |
| model = ModelCatalog().load_model("slim-summary-tiny-tool") |
| response = model.function_call(text_sample) |
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| # this one line will download the model and run a series of tests |
| ModelCatalog().tool_test_run("slim-summary-tiny-tool", verbose=True) |
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| Note: please review [**config.json**](https://huggingface.co/llmware/slim-summary-tiny-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) |
