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transformers/docs/source/en/model_doc/opt.md

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+*This model was released on 2022-05-02 and added to Hugging Face Transformers on 2022-05-12.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+           <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-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">
+           <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# OPT
+
+[OPT](https://huggingface.co/papers/2205.01068) is a suite of open-source decoder-only pre-trained transformers whose parameters range from 125M to 175B. OPT models are designed for causal language modeling and aim to enable responsible and reproducible research at scale. OPT-175B is comparable in performance to GPT-3 with only 1/7th the carbon footprint.
+
+You can find all the original OPT checkpoints under the [OPT](https://huggingface.co/collections/facebook/opt-66ed00e15599f02966818844) collection.
+
+> [!TIP]
+> This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ), [ybelkada](https://huggingface.co/ybelkada), and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
+>
+> Click on the OPT models in the right sidebar for more examples of how to apply OPT 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">
+
+```py
+import torch
+from transformers import pipeline
+
+pipeline = pipeline(task="text-generation", model="facebook/opt-125m", dtype=torch.float16, device=0)
+pipeline("Once upon a time, in a land far, far away,", max_length=50, num_return_sequences=1)
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", dtype=torch.float16, device_map="auto", attn_implementation="sdpa")
+tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+
+prompt = ("Once upon a time, in a land far, far away, ")
+
+model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(**model_inputs, max_new_tokens=30, do_sample=False)
+tokenizer.batch_decode(generated_ids)[0]
+```
+
+</hfoption>
+<hfoption id="transformers CLI">
+
+```py
+echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model facebook/opt-125m --device 0
+```
+
+</hfoption>
+</hfoptions>
+
+Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
+
+The example below uses [bitsandbytes](..quantization/bitsandbytes) to quantize the weights to 8-bits.
+
+```py
+import torch
+from transformers import BitsAndBytesConfig, AutoTokenizer, AutoModelForCausalLM, infer_device
+
+device = infer_device()
+
+bnb_config = BitsAndBytesConfig(load_in_8bit=True)
+model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", dtype=torch.float16, attn_implementation="sdpa", quantization_config=bnb_config).to(device)
+tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b")
+
+prompt = ("Once upon a time, in a land far, far away, ")
+
+model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(**model_inputs, max_new_tokens=30, do_sample=False)
+tokenizer.batch_decode(generated_ids)[0]
+```
+
+## Notes
+
+- OPT adds an `EOS` token `</s>` to the beginning of every prompt.
+
+- The `head_mask` argument is ignored if the attention implementation isn't `"eager"`. Set `attn_implementation="eager"` to enable the `head_mask`.
+
+## Resources
+
+- Refer to this [notebook](https://colab.research.google.com/drive/1jCkpikz0J2o20FBQmYmAGdiKmJGOMo-o?usp=sharing) for an example of fine-tuning OPT with PEFT, bitsandbytes, and Transformers.
+- The [How 🤗 Accelerate runs very large models thanks to PyTorch](https://huggingface.co/blog/accelerate-large-models) blog post demonstrates how to run OPT for inference.
+
+## OPTConfig
+
+[[autodoc]] OPTConfig
+
+## OPTModel
+
+[[autodoc]] OPTModel
+    - forward
+
+## OPTForCausalLM
+
+[[autodoc]] OPTForCausalLM
+    - forward
+
+## OPTForSequenceClassification
+
+[[autodoc]] OPTForSequenceClassification
+    - forward
+
+## OPTForQuestionAnswering
+
+[[autodoc]] OPTForQuestionAnswering
+    - forward