wehub-resource-sync
100 lines
6.9 KiB
Python
100 lines
6.9 KiB
Python
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""" This example shows how to use 7 different SLIM function calling models fine-tuned on top of Phi-3:
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-- Extraction - slim-extract-phi-3-gguf - generates python dictionary with 'key' and 'value'
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-- Summarization - slim-summary-phi-3-gguf - generates python list with key bullet-point summary
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-- XSUM (titles) - slim-xsum-phi-3-gguf - generates python dictionary with 'xsum' key
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-- Boolean - slim-boolean-phi-3-gguf - generate python dictionary with "answer" & "explanation" keys
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-- Sentiment-NER - slim-sa-ner-phi-3-gguf - generates python dictionary with "sentiment" and selected ner keys
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-- Question-Gen - slim-q-gen-phi-3-tool - generates python dictionary with "question" key
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-- Question-Answer - slim-qa-gen-phi-3-tool - generates python dictionary with "question" and "answer" key
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The design of these models is to simplify both the input prompt and output to enable easy integration into
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programmatic workflows.
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"""
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from llmware.models import ModelCatalog
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# sample text passage that will be used as the basis for the function call analysis
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context_passage = ("Best Buy surpassed Wall Street’s revenue and earnings expectations for the holiday quarter on "
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"Thursday, even as the company navigated through a period of tepid consumer electronics demand. "
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"But the retailer warned of another year of softer sales and said it would lay off workers and "
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"cut other costs across the business. CEO Corie Barry offered few specifics, but said the "
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"company has to make sure its workforce and stores match customers’ changing shopping habits. "
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"Cuts will free up capital to invest back into the business and in newer areas, such as artificial "
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"intelligence, she added. “This is giving us some of that space to be able to reinvest into "
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"our future and make sure we feel like we are really well positioned for the industry to "
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"start to rebound,” she said on a call with reporters. For this fiscal year, Best Buy anticipates "
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"revenue will range from $41.3 billion to $42.6 billion. That would mark a drop from the most "
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"recently ended fiscal year, when full-year revenue totaled $43.45 billion. It said comparable "
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"sales will range from flat to a 3% decline. The retailer plans to close 10 to 15 stores "
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"this year after shuttering 24 in the past fiscal year. One challenge that will affect sales "
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"in the year ahead: it is a week shorter. Best Buy said the extra week in the past fiscal "
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"year lifted revenue by about $735 million and boosted diluted earnings per share by about "
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"30 cents. Shares of Best Buy closed more than 1% higher Thursday after briefly touching "
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"a 52-week high of $86.11 earlier in the session. Here’s what the consumer electronics "
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"retailer reported for its fiscal fourth quarter of 2024 compared with what Wall Street was "
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"expecting, based on a survey of analysts by LSEG, formerly known as Refinitiv: "
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"Earnings per share: $2.72, adjusted vs. $2.52 expected Revenue: $14.65 billion vs. $14.56 "
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"billion expected A dip in demand, but a better-than-feared holiday Best Buy has dealt "
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"with slower demand in part due to the strength of its sales during the pandemic. Like "
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"home improvement companies, Best Buy saw outsized spending as shoppers were stuck at "
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"home. Plus, many items that the retailer sells like laptops, refrigerators and home "
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"theater systems tend to be pricier and less frequent purchases. The retailer has cited other "
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"challenges, too: Shoppers have been choosier about making big purchases while dealing "
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"with inflation-driven higher prices of food and more. Plus, they’ve returned to "
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"splitting their dollars between services and goods after pandemic years of little "
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"activity. Even so, Best Buy put up a holiday quarter that was better than feared. "
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"In the three-month period that ended Feb. 3, the company’s net income fell by 7% to "
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"$460 million, or $2.12 per share, from $495 million, or $2.23 per share in the year-ago "
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"period. Revenue dropped from $14.74 billion a year earlier. Comparable sales, a metric that "
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"includes sales online and at stores open at least 14 months, declined 4.8% during the "
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"quarter as shoppers bought fewer appliances, mobile phones, tablets and home theater "
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"setups than the year-ago period. Gaming, on the other hand, was a strong sales "
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"category in the holiday quarter.")
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# for convenience to execute a 'loop', we will set up a dictionary with each function call, and the associated
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# model and parameters that are being passed to the model
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phi3_function_call_models = {
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# extract model will look for the 'key' in the params, and return the 'value' found in the text
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"extract": {"model": "slim-extract-phi-3-gguf", "params": ["net income"]},
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# summary model will return a python list with key summary points related to the parameter
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"summary": {"model": "slim-summary-phi-3-gguf", "params": ["financial highlights"]},
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# xsum model produces an 'extreme summarization', e.g. a headline or title
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"xsum": {"model": "slim-xsum-phi-3-gguf", "params": ["xsum"]},
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# boolean model is designed to answer yes/no questions
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"boolean": {"model": "slim-boolean-phi-3-gguf", "params": ["Is Best Buy closing stores? (explain)"]},
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# sentiment-ner model returns several keys for sentiment and ner attributes (e.g., people, place, organization)
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"sentiment-ner": {"model": "slim-sa-ner-phi-3-gguf", "params": ["sentiment", "people"]},
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# q-gen model generates a question from the context passage
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"q-gen": {"model": "slim-q-gen-phi-3-tool", "params": ["question"]},
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# qa-gen model generates question and answer from the context passage
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"qa-gen": {"model": "slim-qa-gen-phi-3-tool", "params": ["question, answer"]}
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}
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for function, model in phi3_function_call_models.items():
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print(f"\nfunction: {function} - model - {model['model']} - params - {model['params']}")
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slim_model = ModelCatalog().load_model(model["model"], temperature=0.0, sample=False)
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# note: this is the line doing all of the work - each model has been fine-tuned as a 'specialist' for
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# its function, so the only required inputs are the source context passage, and the specific parameters to be used
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response = slim_model.function_call(context_passage, params=model["params"])
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print("response: ", response)
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