Update README.md

README.md

@@ -23,8 +23,8 @@ This model can be easily loaded using the `AutoModelForCausalLM` functionality.
 For regular causal sampling, simply generate completions given the context:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-3.7B")
-model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-3.7B", trust_remote_code=True, revision="main")
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-3_7B")
+model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-3_7B", trust_remote_code=True, revision="main")
 
 text = "def hello_world():"
 input_ids = tokenizer(text, return_tensors="pt").input_ids
@@ -56,8 +56,8 @@ The final snippet looks as follows:
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-3.7B")
-model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-3.7B", trust_remote_code=True, revision="main")
+tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-3_7B")
+model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-3_7B", trust_remote_code=True, revision="main")
 
 
 def format(prefix, suffix):