wehub-resource-sync
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
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""" This example illustrates how to use the slim-boolean model. In this case, we will use test documents provided
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in the model package, which will be cached locally the first time that you load the model. """
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from llmware.models import ModelCatalog
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# load the model - best results with sample=False, but feel free to experiment
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model = ModelCatalog().load_model("slim-boolean-tool",sample=False, temperature=0.0, max_output=200)
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# get the test set packaged with the model
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test_set = ModelCatalog().get_test_script("slim-boolean-tool")
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# iterate through the test set
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for i, sample in enumerate(test_set):
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# add optional "explain" parameter to each question
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question = sample["question"] + " (explain)"
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# key line: invoke function call on the model, with boolean function, and pass the question as the parameter
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response = model.function_call(sample["context"], function="boolean", params=[question])
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print("response: ", response)
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# analyze the logits
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analysis = ModelCatalog().get_fx_scores(response,"slim-boolean-tool")
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# display to the screen
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print("\nllm_response: ", i, question, response["llm_response"])
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print("analysis: ", analysis)
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