Update README.md (#4)
Browse files- Update README.md (202b3c8eb72f613a92e0faf0b148ac30f72b412d)
README.md
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@@ -71,10 +71,10 @@ Find below some example scripts on how to use the model in `transformers`:
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```python
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from transformers import AutoTokenizer,
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tokenizer = AutoTokenizer.from_pretrained("google/switch-base-256")
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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-base-256")
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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-base-256")
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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-base-256")
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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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from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("google/switch-base-256")
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model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-256")
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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-base-256")
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model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-256", device_map="auto")
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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-base-256")
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model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-256", device_map="auto", torch_dtype=torch.float16)
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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-base-256")
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model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-256", device_map="auto")
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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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