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+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+*This model was contributed to Hugging Face Transformers on 2023-06-20.*
+
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+    <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+  </div>
+</div>
+
+# GPT
+
+[GPT (Generative Pre-trained Transformer)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) ([blog post](https://openai.com/index/language-unsupervised/)) focuses on effectively learning text representations and transferring them to tasks. This model trains the Transformer decoder to predict the next word, and then fine-tuned on labeled data.
+
+GPT can generate high-quality text, making it well-suited for a variety of natural language understanding tasks such as textual entailment, question answering, semantic similarity, and document classification.
+
+You can find all the original GPT checkpoints under the [OpenAI community](https://huggingface.co/openai-community/openai-gpt) organization.
+
+> [!TIP]
+> Click on the GPT models in the right sidebar for more examples of how to apply GPT to different language tasks.
+
+The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+generator = pipeline(task="text-generation", model="openai-community/openai-gpt", device=0)
+output = generator("The future of AI is", max_length=50, do_sample=True)
+print(output[0]["generated_text"])
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("openai-community/openai-gpt")
+model = AutoModelForCausalLM.from_pretrained("openai-community/openai-gpt", device_map="auto")
+
+inputs = tokenizer("The future of AI is", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_length=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+</hfoption>
+</hfoptions>
+
+## Notes
+
+- Inputs should be padded on the right because GPT uses absolute position embeddings.
+
+## OpenAIGPTConfig
+
+[[autodoc]] OpenAIGPTConfig
+
+## OpenAIGPTModel
+
+[[autodoc]] OpenAIGPTModel
+    - forward
+
+## OpenAIGPTLMHeadModel
+
+[[autodoc]] OpenAIGPTLMHeadModel
+    - forward
+
+## OpenAIGPTDoubleHeadsModel
+
+[[autodoc]] OpenAIGPTDoubleHeadsModel
+    - forward
+
+## OpenAIGPTForSequenceClassification
+
+[[autodoc]] OpenAIGPTForSequenceClassification
+    - forward
+
+## OpenAIGPTTokenizer
+
+[[autodoc]] OpenAIGPTTokenizer
+
+## OpenAIGPTTokenizerFast
+
+[[autodoc]] OpenAIGPTTokenizerFast