 How to use  You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:  ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)  [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},  {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},  {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},  {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},  {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ```  Here is how to use this model to get the features of a given text in PyTorch:  ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ```  and in TensorFlow:  ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ```  