diff --git a/lora_ft/README.md b/lora_ft/README.md
index d1ddb82e..3bf8ed6e 100644
--- a/lora_ft/README.md
+++ b/lora_ft/README.md
@@ -4,8 +4,8 @@ Here we provide the script for the lora fine-tuning experiments in the paper. Th
 This codebase is based on [run_clm.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#gpt-2gpt-and-causal-language-modeling). Here we adapt this code with LoRA fine-tuning on the C4 training dataset. Some custom changes we make in the code include:
 - [loc 1](https://github.com/locuslab/wanda/blob/main/lora_ft/finetune_lm.py#L374): set up LLaMA-7B for LoRA fine-tuning;
 - [loc 2](https://github.com/locuslab/wanda/blob/main/lora_ft/finetune_lm.py#L521): set up training arguments for Trainer.
-- [loc 3](https://github.com/locuslab/wanda/blob/main/lora_ft/finetune_lm.py#L364): we are using the tokenizer from vicuna, which are the same as the original LLaMA tokenizer but also fix the issues of some special tokens.
-- [loc 4](https://github.com/locuslab/wanda/blob/main/lora_ft/finetune_lm.py#L319): location to load the c4 training dataset.
+- [loc 3](https://github.com/locuslab/wanda/blob/main/lora_ft/finetune_lm.py#L364): load the tokenizer from vicuna, which are the same as the original LLaMA tokenizer but also fix the issues of some special tokens.
+- [loc 4](https://github.com/locuslab/wanda/blob/main/lora_ft/finetune_lm.py#L319): load the c4 training dataset.
 
 To train a LoRA adapter, run the command:
 ```sh