Update README.md (#5)
Browse files- Update README.md (087e6e451f691d5363f53b48ca3c1bbce649a2b2)
README.md
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@@ -71,10 +71,10 @@ Find below some example scripts on how to use the model in `transformers` - bear
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```python
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# pip install accelerate
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from transformers import AutoTokenizer,
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tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
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model =
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input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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```python
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# pip install accelerate
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from transformers import AutoTokenizer,
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tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
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model =
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input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
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```python
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# pip install accelerate
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from transformers import AutoTokenizer,
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tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
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model =
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input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
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```python
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# pip install bitsandbytes accelerate
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from transformers import AutoTokenizer,
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tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
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model =
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input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
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```python
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# pip install accelerate
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from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
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model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", offload_folder=<OFFLOAD_FOLDER>)
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input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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```python
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# pip install accelerate
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from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
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model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", offload_folder=<OFFLOAD_FOLDER>)
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input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
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```python
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# pip install accelerate
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from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
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model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", torch_dtype=torch.bfloat16, offload_folder=<OFFLOAD_FOLDER>)
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input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
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```python
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# pip install bitsandbytes accelerate
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from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
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model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", offload_folder=<OFFLOAD_FOLDER>)
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input_text = "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
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