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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_NAME = "microsoft/Phi-4-mini-instruct"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
def generate_text(prompt, model, tokenizer, max_length=512, temperature=1, top_k=50, top_p=0.95):
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(
inputs,
max_length=max_length,
temperature=temperature,
top_k=top_k,
top_p=top_p,
do_sample=True
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return generated_text
def main():
# Define your prompt
prompt = "According to all known laws of aviation, there is no way a bee should be able to fly."
generated_text = generate_text(prompt, model, tokenizer)
print(generated_text)
if __name__ == "__main__":
main()
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