Update README.md

README.md

@@ -52,16 +52,16 @@ In addition to MentaLLaMA-33B-lora, the MentaLLaMA project includes another mode
 You can use the MentaLLaMA-33B-lora model in your Python project with the Hugging Face Transformers library. Here is a simple example of how to load the model:
 
 Since our model is based on the Vicuna-33B foundation model, you need to first download the Vicuna-33B model [here](https://huggingface.co/lmsys/vicuna-33b-v1.3), 
-and put it under the `./Vicuna-33B` dir.
+and put it under the `./vicuna-33B` dir. Then download the MentaLLaMA-33B-lora weights and put it under the `./MentaLLaMA-33B-lora` dir.
 
 ```python
-from transformers import LlamaTokenizer, LlamaForCausalLM
-tokenizer = LlamaTokenizer.from_pretrained('klyang/MentaLLaMA-chat-13B')
-model = LlamaForCausalLM.from_pretrained('klyang/MentaLLaMA-chat-13B', device_map='auto')
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+peft_model = AutoPeftModelForCausalLM.from_pretrained("./MentaLLaMA-33B-lora")
+tokenizer = AutoTokenizer.from_pretrained('./MentaLLaMA-33B-lora')
 ```
 
-In this example, LlamaTokenizer is used to load the tokenizer, and LlamaForCausalLM is used to load the model. The `device_map='auto'` argument is used to automatically
-use the GPU if it's available.
+In this example, AutoPeftModelForCausalLM can automatically load the base model and the lora weights from the downloaded dir, and AutoTokenizer can load the tokenizer.
 
 ## License