Update README.md

README.md

@@ -8,23 +8,6 @@ FastESM is a Huggingface compatible plug in version of ESM2 rewritten with a new
 
 Load any ESM2 models into a FastEsm model to dramatically speed up training and inference without **ANY** cost in performance.
 
-## Use with 🤗 transformers
-```python
-from transformers import AutoModel, AutoModelForMaskedLM, AutoModelForSequenceClassification, AutoModelForTokenClassification # any of these work
-
-model_dict = {
-    'ESM2-8': 'facebook/esm2_t6_8M_UR50D',
-    'ESM2-35': 'facebook/esm2_t12_35M_UR50D',
-    'ESM2-150': 'facebook/esm2_t30_150M_UR50D',
-    'ESM2-650': 'facebook/esm2_t33_650M_UR50D',
-    'ESM2-3B': 'facebook/esm2_t36_3B_UR50D',
-    'ESM2-15B': 'facebook/esm2_t48_15B_UR50D',
-}
-
-model = AutoModelForMaskedLM.from_pretrained(model_dict['ESM2-8'], trust_remote_code=True)
-tokenizer = model.tokenizer
-```
-
 Outputting attention maps (or the contact prediction head) is not natively possible with SDPA. You can still pass ```output_attentions``` to have attention calculated manually and returned.
 Various other optimizations also make the base implementation slightly different than the one in transformers.