diff --git a/README.md b/README.md
index db6ea25..dfdb337 100644
--- a/README.md
+++ b/README.md
@@ -92,14 +92,26 @@ All performance is based on greedy decoding with COT. We notice that the perform
 
 # Inference
 
+## LMDeploy
+We suggest using [LMDeploy](https://github.com/InternLM/LMDeploy)(>=0.2.1) for inference.
 ```python
-from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
-import torch
+from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
 
-model_dir = snapshot_download("Shanghai_AI_Laboratory/internlm2-math-20b")
-tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True)
+backend_config = TurbomindEngineConfig(model_name='internlm2-chat-7b', tp=1, cache_max_entry_count=0.3)
+chat_template = ChatTemplateConfig(model_name='internlm2-chat-7b', system='', eosys='', meta_instruction='')
+pipe = pipeline(model_path='internlm/internlm2-math-7b', chat_template_config=chat_template, backend_config=backend_config)
+
+problem = '1+1='
+result = pipe([problem], request_output_len=1024, top_k=1)
+```
+
+## Huggingface
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-math-20b", 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, device_map="auto",  trust_remote_code=True, torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-math-20b", trust_remote_code=True, torch_dtype=torch.float16).cuda()
 model = model.eval()
 response, history = model.chat(tokenizer, "1+1=", history=[], meta_instruction="")
 print(response)