google
/

switch-c-2048

Text2Text Generation

Transformers

PyTorch

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switch_transformers

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Update README.md

by ybelkada - opened Nov 22, 2022

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@@ -71,10 +71,10 @@ Find below some example scripts on how to use the model in `transformers` - bear
 ```python
 # pip install accelerate
-from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
 tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
-model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", offload_folder=<OFFLOAD_FOLDER>)
 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>."
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids
@@ -93,10 +93,10 @@ print(tokenizer.decode(outputs[0]))
 ```python
 # pip install accelerate
-from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
 tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
-model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", offload_folder=<OFFLOAD_FOLDER>)
 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>."
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
@@ -117,10 +117,10 @@ print(tokenizer.decode(outputs[0]))
 ```python
 # pip install accelerate
-from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
 tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
-model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", torch_dtype=torch.bfloat16, offload_folder=<OFFLOAD_FOLDER>)
 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>."
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
@@ -139,10 +139,10 @@ print(tokenizer.decode(outputs[0]))
 ```python
 # pip install bitsandbytes accelerate
-from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
 tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
-model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", offload_folder=<OFFLOAD_FOLDER>)
 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>."
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)

 ```python
 # pip install accelerate
+from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
 tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
+model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", offload_folder=<OFFLOAD_FOLDER>)
 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>."
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids
 ```python
 # pip install accelerate
+from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
 tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
+model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", offload_folder=<OFFLOAD_FOLDER>)
 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>."
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
 ```python
 # pip install accelerate
+from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
 tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
+model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", torch_dtype=torch.bfloat16, offload_folder=<OFFLOAD_FOLDER>)
 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>."
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
 ```python
 # pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
 tokenizer = AutoTokenizer.from_pretrained("google/switch-c-2048")
+model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-c-2048", device_map="auto", offload_folder=<OFFLOAD_FOLDER>)
 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>."
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)