 Simple usage sample code  ```python  !pip install tokenizers==0.10.3 transformers==4.8.0  from transformers import AutoTokenizer, AutoModelForCausalLM    tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-xl-poetry") model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-xl-poetry", pad_token_id=tokenizer.eos_token_id)  prompt_text = "אני אוהב שוקולד ועוגות" max_len = 512 sample_output_num = 3 seed = 1000  import numpy as np import torch  device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()  print(f"device: {device}, n_gpu: {n_gpu}")  np.random.seed(seed) torch.manual_seed(seed) if n_gpu > 0:     torch.cuda.manual_seed_all(seed)  model.to(device)  encoded_prompt = tokenizer.encode(     prompt_text, add_special_tokens=False, return_tensors="pt")  encoded_prompt = encoded_prompt.to(device)  if encoded_prompt.size()[-1] == 0:         input_ids = None else:         input_ids = encoded_prompt  print("input_ids = " + str(input_ids))  if input_ids != None:   max_len += len(encoded_prompt[0])   if max_len > 2048:     max_len = 2048  print("Updated max_len = " + str(max_len))  stop_token = "<|endoftext|>" new_lines = "\n\n\n"  sample_outputs = model.generate(     input_ids,     do_sample=True,      max_length=max_len,      top_k=50,      top_p=0.95,      num_return_sequences=sample_output_num )  print(100 * '-' + "\n\t\tOutput\n" + 100 * '-') for i, sample_output in enumerate(sample_outputs):    text = tokenizer.decode(sample_output, skip_special_tokens=True)      