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return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) print(tokenizer.decode(outputs[0])) Conclusion RecurrentGemma, propelled by the Griffin architecture and seamlessly integrated with PyTorch, heralds a new era in efficient open language modeling.