model = AutoModelForCausalLM.from_pretrained("llmware/slim-q-gen-tiny")
tokenizer = AutoTokenizer.from_pretrained("llmware/slim-q-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 8% yesterday?']} }  

# 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)

from llmware.models import ModelCatalog
slim_model = ModelCatalog().load_model("llmware/slim-q-gen-tiny", sample=True, temperature=0.7)  
response = slim_model.function_call(text,params=["boolean"], function="generate")  

print("llmware - llm_response: ", response)