Update README.md

README.md

@@ -154,8 +154,8 @@ Find below some example scripts on how to use the model in `transformers`:
 
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
-model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
+tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
+model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl")
 
 input_text = "translate English to German: How old are you?"
 input_ids = tokenizer.encode(input_text, return_tensors="pt").input_ids
@@ -175,8 +175,8 @@ print(tokenizer.decode(outputs[0]))
 # pip install accelerate
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
-model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto")
+tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
+model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto")
 
 input_text = "translate English to German: How old are you?"
 input_ids = tokenizer.encode(input_text, return_tensors="pt").input_ids.to("cuda")
@@ -199,8 +199,8 @@ print(tokenizer.decode(outputs[0]))
 import torch
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
-model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto", torch_dtype=torch.float16)
+tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
+model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", torch_dtype=torch.float16)
 
 input_text = "translate English to German: How old are you?"
 input_ids = tokenizer.encode(input_text, return_tensors="pt").input_ids.to("cuda")
@@ -220,8 +220,8 @@ print(tokenizer.decode(outputs[0]))
 # pip install bitsandbytes accelerate
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
-tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
-model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto", load_in_8bit=True)
+tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-xl")
+model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_map="auto", load_in_8bit=True)
 
 input_text = "translate English to German: How old are you?"
 input_ids = tokenizer.encode(input_text, return_tensors="pt").input_ids.to("cuda")
@@ -292,7 +292,7 @@ For full details, please check the [research paper](https://arxiv.org/pdf/2210.1
 
 ## Results 
 
-For full results for FLAN-T5-Large, see the [research paper](https://arxiv.org/pdf/2210.11416.pdf), Table 3.
+For full results for FLAN-T5-XL, see the [research paper](https://arxiv.org/pdf/2210.11416.pdf), Table 3.
 
 # Environmental Impact