from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("jinymusim/TinyLlama-Czech-Poet")
model = AutoModelForCausalLM.from_pretrained("jinymusim/TinyLlama-Czech-Poet")

# Input Poet Start
poet_start = '<|AUTHOR|> Adámek, Bohumil'
poet_start = poet_start.strip()
tokenized_poet_start = tokenizer.encode(poet_start, return_tensors='pt')

# generated a continuation to it
out = model.generate(tokenized_poet_start, 
                                max_length=256,
                                do_sample=True,
                                top_k=50
                                early_stopping=True,
                                pad_token_id= tokenizer.pad_token_id,
                                eos_token_id = tokenizer.eos_token_id)

# Decode Poet
decoded_cont = tokenizer.decode(out[0], skip_special_tokens=True)

print(decoded_cont)

Structure of outputs

Outputs are structured in following way:

<|AUTHOR|> AUTHOR
<|TITLE|> TITLE
<|YEAR|> YEAR
<|STROPHE_START|>
<|METER|> METER
<|RHYME|> RHYME SCHEMA
STROPHE
<|STROPHE_END|>
<|STROPHE_START|>
<|METER|> METER
<|RHYME|> RHYME SCHEMA
STROPHE
<|STROPHE_START|>