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*This model was contributed to Hugging Face Transformers on 2023-06-20.*

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# 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
