llmware/slim-boolean

SLIM-BOOLEAN

<!-- Provide a quick summary of what the model is/does. -->

slim-boolean is an experimental model designed to implement a boolean question answering function call using a 2.7B parameter specialized model. As an input, the model takes a context passage, a yes-no question, and an optional (explain) parameter, and as output, the model generates a python dictionary with two keys - 'answer' which contains the 'yes/no' classification, and 'explain' which provides a text snippet from the passage that was the basis for the classification, e.g.:

&nbsp;&nbsp;&nbsp;&nbsp;{'answer': ['yes'], 'explanation': ['the results exceeded expectations by 3%'] }

This model is fine-tuned on top of **llmware/bling-stable-lm-3b-4e1t-v0**, which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt.

For fast inference, we would recommend using the'quantized tool' version, e.g., **'slim-boolean-tool'**.

Prompt format:

function = "boolean" params = "{insert yes-no-question} (explain)" prompt = "<human> " + {text} + "\n" + &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp;"<{function}> " + {params} + "</{function}>" + "\n<bot>:"

<details> <summary>Transformers Script </summary>

model = AutoModelForCausalLM.frompretrained("llmware/slim-boolean") tokenizer = AutoTokenizer.frompretrained("llmware/slim-boolean")

function = "boolean" params = "did tesla stock price increase? (explain) "

text = "Tesla stock declined yesterday 8% in premarket trading after a poorly-received event in San Francisco yesterday, in which the company indicated a likely shortfall in revenue."

prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"

inputs = tokenizer(prompt, returntensors="pt") startofinput = len(inputs.inputids[0])

outputs = model.generate( inputs.inputids.to('cpu'), eostokenid=tokenizer.eostokenid, padtokenid=tokenizer.eostokenid, dosample=True, temperature=0.3, maxnewtokens=100 )

outputonly = tokenizer.decode(outputs[0][startofinput:], skipspecial_tokens=True)

print("output only: ", output_only)

# here's the fun part try: outputonly = ast.literaleval(llmstringoutput) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llmstringoutput)

</details>

<details>

<summary>Using as Function Call in LLMWare</summary>

from llmware.models import ModelCatalog slimmodel = ModelCatalog().loadmodel("llmware/slim-boolean") response = slimmodel.functioncall(text,params=["did the stock price increase? (explain)"], function="boolean")

print("llmware - llm_response: ", response)

</details>

Model Card Contact

Darren Oberst & llmware team

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