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  ---
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- license: cc-by-sa-4.0
 
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  ---
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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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-
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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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@@ -19,7 +18,7 @@ Please see the usage notes at: [**slim-summary**](https://huggingface.co/llmwar
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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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+ license: apache-2.0
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+ inference: false
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  ---
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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