Update README.md

README.md

@@ -74,8 +74,8 @@ Find below some example scripts on how to use the model in `transformers`:
 
 from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
 
-tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
-model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-8")
+tokenizer = AutoTokenizer.from_pretrained("google/switch-base-16")
+model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-16")
 
 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
@@ -96,8 +96,8 @@ print(tokenizer.decode(outputs[0]))
 # pip install accelerate
 from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
 
-tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
-model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-8", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("google/switch-base-16")
+model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-16", device_map="auto")
 
 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)
@@ -120,8 +120,8 @@ print(tokenizer.decode(outputs[0]))
 # pip install accelerate
 from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
 
-tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
-model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-8", device_map="auto", torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained("google/switch-base-16")
+model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-16", device_map="auto", torch_dtype=torch.float16)
 
 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)
@@ -142,8 +142,8 @@ print(tokenizer.decode(outputs[0]))
 # pip install bitsandbytes accelerate
 from transformers import AutoTokenizer, SwitchTransformersConditionalGeneration
 
-tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
-model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-8", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("google/switch-base-16")
+model = SwitchTransformersConditionalGeneration.from_pretrained("google/switch-base-16", device_map="auto")
 
 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)