Update README.md

README.md

@@ -76,8 +76,8 @@ Overall, InternLM-20B comprehensively outperforms open-source models in the 13B
 To load the InternLM 7B Chat model using Transformers, use the following code:
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
->>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-20b-chat", trust_remote_code=True)
->>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-20b-chat", trust_remote_code=True).cuda()
+>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-20b", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-20b", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> inputs = tokenizer(["Coming to the beautiful nature, we found"], return_tensors="pt")
 >>> for k,v in inputs.items():
@@ -146,8 +146,8 @@ InternLM 20B 在模型结构上选择了深结构，层数设定为60层，超
 通过以下的代码加载 InternLM 20B 模型
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
->>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-20b-chat", trust_remote_code=True)
->>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-20b-chat", trust_remote_code=True).cuda()
+>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-20b", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-20b", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> inputs = tokenizer(["来到美丽的大自然，我们发现"], return_tensors="pt")
 >>> for k,v in inputs.items():