| from transformers import AutoTokenizer | |
| from retnet.modeling_retnet import RetNetForCausalLM | |
| model = RetNetForCausalLM.from_pretrained("retnet-tinystories") | |
| tokenizer = AutoTokenizer.from_pretrained('gpt2') | |
| tokenizer.model_max_length = 16384 | |
| tokenizer.pad_token = tokenizer.eos_token | |
| tokenizer.unk_token = tokenizer.eos_token | |
| tokenizer.bos_token = tokenizer.eos_token | |
| inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt") | |
| # Generate output with max_length parameter | |
| generation_output = model.generate(**inputs, max_length=50) # Adjust max_length as needed | |
| output = tokenizer.decode(generation_output[0], skip_special_tokens=True) | |
| print(output) |