SLIM-QA-GEN-TINY

slim-qa-gen-tiny implements a specialized function-calling question generation and answer from a context passage, with output in the form of a python dictionary, e.g.,

    {'question': ['What were earnings per share in the most recent quarter?'], 'answer': [$3.36]}

This model is finetuned on top of a tinyllama 1.1b base.

For fast inference use, we would recommend the 'quantized tool' version, e.g., 'slim-qa-gen-tiny-tool'.

Prompt format:

function = "generate"
params = "{'question, answer', 'boolean', or 'multiple choice'}"
prompt = "<human> " + {text} + "\n" +
                      "<{function}> " + {params} + "</{function}>" + "\n<bot>:"

Transformers Script

model = AutoModelForCausalLM.from_pretrained("llmware/slim-qa-gen-tiny")
tokenizer = AutoTokenizer.from_pretrained("llmware/slim-qa-gen-tiny")

function = "generate"
params = "boolean"

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(
    inputs.input_ids.to('cpu'),
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.eos_token_id,
    do_sample=True,
    temperature=0.7,
    max_new_tokens=200
)

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

print("output only: ", output_only)  

[OUTPUT]:  {'llm_response': {'question': ['Did Telsa stock decline more than 5% yesterday?'], 'answer': ['yes']} }  

# here's the fun part
try:
    output_only = ast.literal_eval(llm_string_output)
    print("success - converted to python dictionary automatically")
except:
    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-qa-gen-tiny", sample=True, temperature=0.7)  
response = slim_model.function_call(text,params=["boolean"], function="generate")  

print("llmware - llm_response: ", response)  

Model Card Contact

Darren Oberst & llmware team

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