rename 260m to 260M

README.md

@@ -18,7 +18,7 @@ Check out our [GitHub](https://github.com/HazyResearch/m2/tree/main) for instruc
 You can load this model using Hugging Face `AutoModel`:
 ```python
 from transformers import AutoModelForMaskedLM
-mlm = AutoModelForMaskedLM.from_pretrained('alycialee/m2-bert-260m', trust_remote_code=True)
+mlm = AutoModelForMaskedLM.from_pretrained('alycialee/m2-bert-260M', trust_remote_code=True)
 ```
 
 This model uses the Hugging Face `bert-base-uncased tokenizer`:
@@ -32,7 +32,7 @@ You can use this model with a pipeline for masked language modeling:
 from transformers import AutoModelForMaskedLM, BertTokenizer, pipeline
 
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-mlm = AutoModelForMaskedLM.from_pretrained('alycialee/m2-bert-260m', trust_remote_code=True)
+mlm = AutoModelForMaskedLM.from_pretrained('alycialee/m2-bert-260M', trust_remote_code=True)
 
 unmasker = pipeline('fill-mask', model=mlm, tokenizer=tokenizer)
 unmasker('Every morning, I enjoy a cup of [MASK] to start my day.')
@@ -44,7 +44,7 @@ This model requires `trust_remote_code=True` to be passed to the `from_pretraine
 
 ```python
 mlm = AutoModelForMaskedLM.from_pretrained(
-   'alycialee/m2-bert-260m',
+   'alycialee/m2-bert-260M',
    trust_remote_code=True,
    revision='85de208',
 )

config.json

@@ -1,5 +1,5 @@
 {
-    "_name_or_path": "alycialee/m2-bert-260m",
+    "_name_or_path": "alycialee/m2-bert-260M",
     "alibi_starting_size": 512,
     "architectures": [
         "BertForMaskedLM"