Update README.md

README.md

@@ -87,18 +87,8 @@ We suggest using [LMDeploy](https://github.com/InternLM/LMDeploy)(>=0.2.1) for i
 from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
 
 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='', 
-                                user='<|im_start|>user\n', 
-                                assistant='<|im_start|>assistant\n', 
-                                eoh='<|im_end|>\n', 
-                                eoa='<|im_end|>\n',
-                                stop_words=['<|im_end|>', '<|action_end|>'])
-pipe = pipeline(model_path='internlm/internlm2-math-7b',
-                chat_template_config=chat_template,
-                backend_config=backend_config)
+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)
@@ -112,7 +102,7 @@ tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-math-7b", trust_re
 # 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("internlm/internlm2-math-7b", trust_remote_code=True, torch_dtype=torch.float16).cuda()
 model = model.eval()
-response, history = model.chat(tokenizer, "1+1=", history=[])
+response, history = model.chat(tokenizer, "1+1=", history=[], meta_instruction="")
 print(response)
 ```