wehub-resource-sync
46 lines
2.0 KiB
Python
46 lines
2.0 KiB
Python
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""" This example illustrates how to use slim-summary models to easily create summaries generated as a list of
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summary points for easy integration into multi-step workflows. There are several slim-summary function-calling
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models available in different sizes and based on leading underlying base models. """
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from llmware.models import ModelCatalog
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# three slim-summary function calling models available
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slim_summary_models = ["slim-summary-tool", # original - stablelm-3b
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"slim-summary-tiny-tool", # small - tiny-llama (1.1b)
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"slim-summary-phi-3-gguf" # phi-3 - phi-3 (3.8b)
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]
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# load the model and set the sampling and output parameters
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model = ModelCatalog().load_model("slim-summary-tool",
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sample=False,
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temperature=0.0,
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max_output=200)
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# get the test data set packaged with the model
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test_script = ModelCatalog().get_test_script("slim-summary-tool")
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# iterate through the samples
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for i, sample in enumerate(test_script):
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# invoke function call on the model, passing the context passage and the 'summarize' function
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# the parameter can be a generic phrase, e.g., 'key points' or 'brief description' or 'summary'
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# if the material has a lot of numeric data points, try the parameter 'data points' or 'financial data points'
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# if you are looking for a single line of output, try 'brief description'
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# the number in ( ) is optional - but is intended to guide the model to provide with a list with the requested
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# number of elements
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response = model.function_call(sample["context"], function="summarize", params=["data points (5)"])
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# display the response
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print("\nresponse: ", response)
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# check how effectively the model mapped the output to the requested number of points
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r = response["llm_response"]
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for j, entries in enumerate(r):
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print("summary points: ", j, entries)
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