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*This model was released on 2022-05-02 and added to Hugging Face Transformers on 2022-05-12 and contributed by [ArthurZ](https://huggingface.co/ArthurZ), [ybelkada](https://huggingface.co/ybelkada), and [patrickvonplaten](https://huggingface.co/patrickvonplaten).*

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# OPT

[OPT](https://huggingface.co/papers/2205.01068) is a suite of decoder-only pre-trained transformers with parameter sizes ranging from 125M to 175B. OPT-175B matches the capabilities of GPT-3 but with a significantly lower carbon footprint during development. The models are fully shared, along with infrastructure challenges and experimental code, enabling broader research access. OPT uses the same architecture as BartDecoder and uniquely adds the EOS token `</s>` at the start of each prompt.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="facebook/opt-125mfacebook/opt-125m", 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("facebook/opt-125m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m", 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

- OPT adds an EOS token `</s>` to the beginning of every prompt.

## OPTConfig

[[autodoc]] OPTConfig

## OPTModel

[[autodoc]] OPTModel
    - forward

## OPTForCausalLM

[[autodoc]] OPTForCausalLM
    - forward

## OPTForSequenceClassification

[[autodoc]] OPTForSequenceClassification
    - forward

## OPTForQuestionAnswering

[[autodoc]] OPTForQuestionAnswering
    - forward

