Update README.md

README.md

@@ -16,14 +16,14 @@ Our overall explanation models along with ablations can be found in our [paper](
 Here is how to use the model in PyTorch:
 ```python
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-tokenizer = AutoTokenizer.from_pretrained("seonghyeonye/flipped_11B")
-model = AutoModelForSeq2SeqLM.from_pretrained("seonghyeonye/flipped_11B")
+tokenizer = AutoTokenizer.from_pretrained("seonghyeonye/flipped_3B")
+model = AutoModelForSeq2SeqLM.from_pretrained("seonghyeonye/flipped_3B")
 inputs = tokenizer.encode("input: this is the best cast iron skillet you will ever buy\noutput: Positive", return_tensors="pt")
 outputs = model.generate(inputs)
 print(tokenizer.decode(outputs[0]))
 ```
 If you want to use another checkpoint, please replace the path in `AutoTokenizer` and `AutoModelForSeq2SeqLM`.
-**Note: the model was trained with bf16 activations. As such, we highly discourage running inference with fp16. fp32 or bf16 should be preferred.**
+**Note: the model was trained with fp32 activations. As such, we highly discourage running inference with fp16.**
 
 # Training procedure
 FLIPPED models are based on [T5](https://huggingface.co/google/t5-v1_1-large), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4).