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122 lines
4.6 KiB
Markdown
122 lines
4.6 KiB
Markdown
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2022-05-02 and contributed to Hugging Face Transformers on 2022-05-12.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# OPT
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[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.
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You can find all the original OPT checkpoints under the [OPT](https://huggingface.co/collections/facebook/opt-66ed00e15599f02966818844) collection.
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> [!TIP]
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> This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ), [ybelkada](https://huggingface.co/ybelkada), and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
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>
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> Click on the OPT models in the right sidebar for more examples of how to apply OPT to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```python
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from transformers import pipeline
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pipeline = pipeline(task="text-generation", model="facebook/opt-125m", device=0)
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pipeline("Once upon a time, in a land far, far away,", max_length=50, num_return_sequences=1)
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```
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</hfoption>
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<hfoption id="AutoModel">
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", device_map="auto", attn_implementation="sdpa")
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tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
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prompt = ("Once upon a time, in a land far, far away, ")
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model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
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generated_ids = model.generate(**model_inputs, max_new_tokens=30, do_sample=False)
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tokenizer.batch_decode(generated_ids)[0]
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```
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</hfoption>
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</hfoptions>
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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.
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The example below uses [bitsandbytes](../quantization/bitsandbytes) to quantize the weights to 8-bits.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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bnb_config = BitsAndBytesConfig(load_in_8bit=True)
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model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", attn_implementation="sdpa", quantization_config=bnb_config, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b")
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prompt = ("Once upon a time, in a land far, far away, ")
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model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
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generated_ids = model.generate(**model_inputs, max_new_tokens=30, do_sample=False)
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tokenizer.batch_decode(generated_ids)[0]
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```
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## Notes
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- OPT adds an `EOS` token `</s>` to the beginning of every prompt.
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## Resources
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- 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.
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- 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.
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## OPTConfig
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[[autodoc]] OPTConfig
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## OPTModel
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[[autodoc]] OPTModel
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- forward
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## OPTForCausalLM
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[[autodoc]] OPTForCausalLM
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- forward
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## OPTForSequenceClassification
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[[autodoc]] OPTForSequenceClassification
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- forward
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## OPTForQuestionAnswering
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[[autodoc]] OPTForQuestionAnswering
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- forward
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