opt-2.7b

18.3K
86
2.7B
4 languages
—
by
facebook
Language Model
OTHER
2.7B params
Fair
18K downloads
Community-tested
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Edge AI:
Mobile
Laptop
Server
7GB+ RAM
Mobile
Laptop
Server
Quick Summary

OPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
3GB+ RAM

Code Examples

How to usepythontransformers
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}]
How to usepythontransformers
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}]
How to usepythontransformers
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}]
How to usepythontransformers
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}]
How to usepythontransformers
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}]
How to usepythontransformers
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}]
How to usepythontransformers
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}]
How to usepythontransformers
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}]
How to usepythontransformers
>>> from transformers import pipeline

>>> generator = pipeline('text-generation', model="facebook/opt-2.7b")
>>> generator("What are we having for dinner?")
[{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}]
pythontransformers
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}]
pythontransformers
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}]
pythontransformers
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}]
pythontransformers
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}]
pythontransformers
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}]
pythontransformers
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}]
pythontransformers
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}]
pythontransformers
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}]
pythontransformers
>>> from transformers import pipeline, set_seed

>>> set_seed(32)
>>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True)
>>> generator("What are we having for dinner?")
[{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}]

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