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slim-extract implements a specialized function-calling customizable 'extract' capability that takes as an input a context passage, a customized key, and outputs a python dictionary with key that corresponds to the customized key, with a value consisting of a list of items extracted from the text corresponding to that key, e.g.,

    {'universities': ['Berkeley, Stanford, Yale, University of Florida, ...'] }

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 use, we would recommend the 'quantized tool' version, e.g., 'slim-extract-tool'.

Prompt format:

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

Transformers Script
model = AutoModelForCausalLM.from_pretrained("llmware/slim-extract")
tokenizer = AutoTokenizer.from_pretrained("llmware/slim-extract")

function = "extract"
params = "company"

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, return_tensors="pt")
start_of_input = len(inputs.input_ids[0])

outputs = model.generate(

output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)

print("output only: ", output_only)  

# here's the fun part
    output_only = ast.literal_eval(llm_string_output)
    print("success - converted to python dictionary automatically")
    print("fail - could not convert to python dictionary automatically - ", llm_string_output)
Using as Function Call in LLMWare
from llmware.models import ModelCatalog
slim_model = ModelCatalog().load_model("llmware/slim-extract")
response = slim_model.function_call(text,params=["company"], function="extract")

print("llmware - llm_response: ", response)

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