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# OpenAI GPT2

## Overview

OpenAI GPT-2 model was proposed in [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) by Alec
Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever. It's a causal (unidirectional)
transformer pretrained using language modeling on a very large corpus of ~40 GB of text data.

The abstract from the paper is the following:

*GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million
web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some
text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks
across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
10X the amount of data.*

Tips:

- GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
- GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be
  observed in the *run_generation.py* example script.
- The model can take the *past_key_values* (for PyTorch) or *past* (for TF) as input, which is the previously computed
  key/value attention pairs. Using this (*past_key_values* or *past*) value prevents the model from re-computing
  pre-computed values in the context of text generation. For PyTorch, see *past_key_values* argument of the
  [`GPT2Model.forward`] method, or for TF the *past* argument of the
  [`TFGPT2Model.call`] method for more information on its usage.
- Enabling the *scale_attn_by_inverse_layer_idx* and *reorder_and_upcast_attn* flags will apply the training stability
  improvements from [Mistral](https://github.com/stanford-crfm/mistral/) (for PyTorch only).

[Write With Transformer](https://transformer.huggingface.co/doc/gpt2-large) is a webapp created and hosted by
Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
different sizes: small, medium, large, xl and a distilled version of the small checkpoint: *distilgpt-2*.

This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://openai.com/blog/better-language-models/).


## GPT2Config

[[autodoc]] GPT2Config

## GPT2Tokenizer

[[autodoc]] GPT2Tokenizer
    - save_vocabulary

## GPT2TokenizerFast

[[autodoc]] GPT2TokenizerFast

## GPT2 specific outputs

[[autodoc]] models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput

[[autodoc]] models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput

## GPT2Model

[[autodoc]] GPT2Model
    - forward
    - parallelize
    - deparallelize

## GPT2LMHeadModel

[[autodoc]] GPT2LMHeadModel
    - forward
    - parallelize
    - deparallelize

## GPT2DoubleHeadsModel

[[autodoc]] GPT2DoubleHeadsModel
    - forward

## GPT2ForSequenceClassification

[[autodoc]] GPT2ForSequenceClassification
    - forward

## GPT2ForTokenClassification

[[autodoc]] GPT2ForTokenClassification
    - forward

## TFGPT2Model

[[autodoc]] TFGPT2Model
    - call

## TFGPT2LMHeadModel

[[autodoc]] TFGPT2LMHeadModel
    - call

## TFGPT2DoubleHeadsModel

[[autodoc]] TFGPT2DoubleHeadsModel
    - call

## TFGPT2ForSequenceClassification

[[autodoc]] TFGPT2ForSequenceClassification
    - call

## TFSequenceClassifierOutputWithPast

[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutputWithPast

## FlaxGPT2Model

[[autodoc]] FlaxGPT2Model
    - __call__

## FlaxGPT2LMHeadModel

[[autodoc]] FlaxGPT2LMHeadModel
    - __call__
