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from transformers import AutoTokenizer, AutoModelForCausalLM
# Load your fine-tuned model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("./lockin_model")
model = AutoModelForCausalLM.from_pretrained("./lockin_model")
# Function to generate yes/no questions
def generate_question(input_text, max_retries=20):
for _ in range(max_retries):
# Add padding and attention mask
inputs = tokenizer(
input_text,
return_tensors="pt",
padding=True,
truncation=True,
return_attention_mask=True
)
output = model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=100,
do_sample=True,
temperature=1.9,
top_p=0.8,
top_k=50,
pad_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
# Remove the input text from the generated output
if generated_text.startswith(input_text):
generated_text = generated_text[len(input_text):].strip()
# If we got a non-empty response and it contains $LOCKIN, return it
if generated_text and "$LOCKIN" in generated_text:
return generated_text
# If all retries failed, return default question
return "Does $LOCKIN look great?"
# Example usage
prompt = "I need a yes/no question about $LOCKIN."
question = generate_question(prompt)
print("Generated Question:", question)
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