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README.md

@@ -42,7 +42,7 @@ print(logits.shape) # (batch_size, num_labels), (2, 2)
 ESM++ weights are fp32 by default. You can load them in fp16 or bf16 like this:
 ```python
 import torch
-model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_large', trust_remote_code=True, torch_dtype=torch.float16) # or torch.bfloat16
+model = AutoModelForMaskedLM.from_pretrained('Synthyra/ESMplusplus_large', trust_remote_code=True, dtype=torch.float16) # or torch.bfloat16
 ```
 
 ## Embed entire datasets with no new code
@@ -156,9 +156,9 @@ We look at various ESM models and their throughput on an H100. Adding efficient
 If you use any of this implementation or work please cite it (as well as the ESMC preprint).
 
 ```
-@misc {ESM++,
+@misc {FastPLMs,
     author       = { Hallee, Logan and Bichara, David and Gleghorn, Jason P.},
-    title        = { ESM++: Efficient and Hugging Face compatible versions of the ESM Cambrian models},
+    title        = { FastPLMs: Fast, efficient, protien language model inference from Huggingface AutoModel.},
     year         = {2024},
     url          = { https://huggingface.co/Synthyra/ESMplusplus_small },
     DOI          = { 10.57967/hf/3726 },