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*This model was released on 2018-09-19 and added to Hugging Face Transformers on 2023-06-20 and contributed by [thomwolf](https://huggingface.co/thomwolf).*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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# GPT

[GPT](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) addresses the challenge of limited labeled data for natural language understanding tasks like textual entailment, question answering, semantic similarity, and document classification. It shows that pretraining a language model generatively on large unlabeled text, followed by task-specific discriminative fine-tuning with task-aware input transformations, can achieve strong performance with minimal architectural changes. This approach outperforms traditional discriminative models designed for individual tasks and improves the state of the art in 9 of 12 evaluated benchmarks. The results demonstrate that a single, task-agnostic model can effectively transfer knowledge across diverse NLU tasks.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="openai-community/openai-gpt", dtype="auto",)
pipeline("Plants create energy through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("openai-community/openai-gpt")
model = AutoModelForCausalLM.from_pretrained("openai-community/openai-gpt", dtype="auto",)

inputs = tokenizer("Plants create energy through a process known as photosynthesis.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## Usage tips

- Pad inputs on the right. GPT uses absolute position embeddings.

## OpenAIGPTConfig

[[autodoc]] OpenAIGPTConfig

## OpenAIGPTTokenizer

[[autodoc]] OpenAIGPTTokenizer
    - save_vocabulary

## OpenAIGPTTokenizerFast

[[autodoc]] OpenAIGPTTokenizerFast

## OpenAI specific outputs

[[autodoc]] models.openai.modeling_openai.OpenAIGPTDoubleHeadsModelOutput

## OpenAIGPTModel

[[autodoc]] OpenAIGPTModel
    - forward

## OpenAIGPTLMHeadModel

[[autodoc]] OpenAIGPTLMHeadModel
    - forward

## OpenAIGPTDoubleHeadsModel

[[autodoc]] OpenAIGPTDoubleHeadsModel
    - forward

## OpenAIGPTForSequenceClassification

[[autodoc]] OpenAIGPTForSequenceClassification
    - forward

