diff --git a/README-ja-JP.md b/README-ja-JP.md
index 5e5b7db..71e1d4d 100644
--- a/README-ja-JP.md
+++ b/README-ja-JP.md
@@ -86,8 +86,8 @@ Transformers を使用して InternLM 7B チャットモデルをロードする
 
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM
->>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b-v1_1", trust_remote_code=True)
->>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b-v1_1", trust_remote_code=True).cuda()
+>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> response, history = model.chat(tokenizer, "こんにちは", history=[])
 >>> print(response)
diff --git a/README-zh-Hans.md b/README-zh-Hans.md
index 62d58d0..7ac8e75 100644
--- a/README-zh-Hans.md
+++ b/README-zh-Hans.md
@@ -182,7 +182,7 @@ InternLM-7B 包含了一个拥有70亿参数的基础模型和一个为实际场
 ```python
 from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
 import torch
-model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b-v1_1', revision='v1.0.0')
+model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b', revision='v1.0.0')
 tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
 model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto",  trust_remote_code=True,torch_dtype=torch.float16)
 model = model.eval()
diff --git a/README.md b/README.md
index e509963..bd58a76 100644
--- a/README.md
+++ b/README.md
@@ -158,8 +158,8 @@ 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-chat-7b-v1_1", trust_remote_code=True)
->>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b-v1_1", trust_remote_code=True).cuda()
+>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True)
+>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm-chat-7b", trust_remote_code=True).cuda()
 >>> model = model.eval()
 >>> response, history = model.chat(tokenizer, "hello", history=[])
 >>> print(response)
@@ -182,7 +182,7 @@ To load the InternLM model using ModelScope, use the following code:
 ```python
 from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
 import torch
-model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b-v1_1', revision='v1.0.0')
+model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b', revision='v1.0.0')
 tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
 model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto",  trust_remote_code=True,torch_dtype=torch.float16)
 model = model.eval()