diff --git a/README.md b/README.md
index b6398f9..89d33ac 100644
--- a/README.md
+++ b/README.md
@@ -80,15 +80,14 @@ transformers >= 4.48
 
 ### Conversation Mode
 
-#### Transformers inference
+#### Modelscope inference
 
 To load the InternLM3 8B Instruct model using Transformers, use the following code:
 
 ```python
 import torch
-from modelscope import AutoTokenizer, AutoModelForCausalLM
-
-model_dir = "internlm/internlm3-8b-instruct"
+from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
+model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
 # model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
@@ -272,9 +271,8 @@ Focus on clear, logical progression of ideas and thorough explanation of your ma
 
 ```python
 import torch
-from modelscope import AutoTokenizer, AutoModelForCausalLM
-
-model_dir = "internlm/internlm3-8b-instruct"
+from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
+model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
 model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
@@ -442,9 +440,8 @@ transformers >= 4.48
 
 ```python
 import torch
-from modelscope import AutoTokenizer, AutoModelForCausalLM
-
-model_dir = "internlm/internlm3-8b-instruct"
+from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
+model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
 # model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
@@ -623,9 +620,8 @@ Focus on clear, logical progression of ideas and thorough explanation of your ma
 
 ```python
 import torch
-from modelscope import AutoTokenizer, AutoModelForCausalLM
-
-model_dir = "internlm/internlm3-8b-instruct"
+from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
+model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
 model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16)