AutoModelForConditionalGeneration > T5ForConditionalGeneration
#3
by
gojiteji
 opened
README.md
CHANGED
@@ 100,9 +100,9 @@ For more efficient memory usage, we advise you to load the model in `8bit` using


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```python

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# pip install accelerate transformers bitsandbytes

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from transformers import

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import torch

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model =

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tokenizer = AutoTokenizer.from_pretrained("google/flanul2")

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input_string = "Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?"

@@ 118,9 +118,9 @@ Otherwise, you can load and run the model in `bfloat16` as follows:


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```python

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# pip install accelerate transformers

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from transformers import

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import torch

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model =

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tokenizer = AutoTokenizer.from_pretrained("google/flanul2")

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input_string = "Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?"



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```python

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# pip install accelerate transformers bitsandbytes

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+
from transformers import T5ForConditionalGeneration, AutoTokenizer

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import torch

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model = T5ForConditionalGeneration.from_pretrained("google/flanul2", device_map="auto", load_in_8bit=True)

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tokenizer = AutoTokenizer.from_pretrained("google/flanul2")

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input_string = "Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?"



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```python

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# pip install accelerate transformers

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+
from transformers import T5ForConditionalGeneration, AutoTokenizer

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import torch

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+
model = T5ForConditionalGeneration.from_pretrained("google/flanul2", torch_dtype=torch.bfloat16, device_map="auto")

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tokenizer = AutoTokenizer.from_pretrained("google/flanul2")

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input_string = "Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch, and bought 6 more, how many apple do they have?"
