transformers/docs/source/en/model_doc/openai-gpt.md

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# GPT

[GPT (Generative Pre-trained Transformer)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) 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
import torch
from transformers import pipeline

generator = pipeline(task="text-generation", model="openai-community/gpt", torch_dtype=torch.float16, 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/gpt")
model = AutoModelForCausalLM.from_pretrained("openai-community/openai-gpt", torch_dtype=torch.float16)

inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

</hfoption>
<hfoption id="transformers-cli">

```bash
echo -e "The future of AI is" | transformers-cli run --task text-generation --model openai-community/openai-gpt --device 0

```
</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

## TFOpenAIGPTModel

[[autodoc]] TFOpenAIGPTModel
- call

## TFOpenAIGPTLMHeadModel

[[autodoc]] TFOpenAIGPTLMHeadModel
- call

## TFOpenAIGPTDoubleHeadsModel

[[autodoc]] TFOpenAIGPTDoubleHeadsModel
- call

## TFOpenAIGPTForSequenceClassification

[[autodoc]] TFOpenAIGPTForSequenceClassification
- call