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使用官方提供的脚本创建ChatGLM3的DEMO：

cd basic_demo
python web_demo_gradio.py

 出现效果异常问题：

====conversation====
 [{'role': 'user', 'content': '你好'}, {'role': 'assistant', 'content': '你好，有什么我可以帮助你的吗？\n\n<|im_end|>'}, {'role': 'user', 'content': '你好'}]

No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format (without BOS/EOS tokens!). If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information.

原因分析：

• 模型版本与代码不匹配，tokenizer_config.json配置文件中缺少prompt模板
• 官方代码存在问题，尚不支持本地模型使用apply_chat_template方法

解决方案：修改tokenizer方式，不要使用apply_chat_template方法，单轮对话可以改用build_chat_input方法

def predict(history, max_length, top_p, temperature):stop = StopOnTokens()messages = []for idx, (user_msg, model_msg) in enumerate(history):if idx == len(history) - 1 and not model_msg:messages.append({"role": "user", "content": user_msg})breakif user_msg:messages.append({"role": "user", "content": user_msg})if model_msg:messages.append({"role": "assistant", "content": model_msg})print("\n\n====conversation====\n", messages)model_inputs = tokenizer.apply_chat_template(messages,add_generation_prompt=True,tokenize=True,return_tensors="pt").to(next(model.parameters()).device)print('debug: old: model_inputs: {}'.format(model_inputs))model_inputs = tokenizer.build_chat_input(messages[-1]['content'], history=None, role="user").input_ids.to(model.device)print('debug: new: model_inputs: {}'.format(model_inputs))streamer = TextIteratorStreamer(tokenizer, timeout=60, skip_prompt=True, skip_special_tokens=True)generate_kwargs = {"input_ids": model_inputs,"streamer": streamer,"max_new_tokens": max_length,"do_sample": True,"top_p": top_p,"temperature": temperature,"stopping_criteria": StoppingCriteriaList([stop]),"repetition_penalty": 1.2,}t = Thread(target=model.generate, kwargs=generate_kwargs)t.start()for new_token in streamer:if new_token != '':history[-1][1] += new_tokenyield history

tokenizer.chat_template介绍

Next time you use apply_chat_template(), it will use your new template! This attribute will be saved in the tokenizer_config.json file, so you can use push_to_hub() to upload your new template to the Hub and make sure everyone’s using the right template for your model!

设置tokenizer.chat_template属性后，下次使用apply_chat_template()时，将使用您的新模板！此属性保存在tokenizer_config.json文件中，因此您可以用push_to_hub()将新模板上传到Hub，确保大家都能使用正确的模板！

If a model does not have a chat template set, but there is a default template for its model class, the ConversationalPipeline class and methods like apply_chat_template will use the class template instead. You can find out what the default template for your tokenizer is by checking the tokenizer.default_chat_template attribute.

如果模型没有设置聊天模板，但有其模型类的默认模板，则ConversationalPipeline类和apply_chat_template等方法将使用类模板代替。你可以通过检查tokenizer.default_chat_template属性来了解你的tokenizer的默认模板是什么。 

def predict(history, max_length, top_p, temperature):stop = StopOnTokens()messages = []for idx, (user_msg, model_msg) in enumerate(history):if idx == len(history) - 1 and not model_msg:messages.append({"role": "user", "content": user_msg})breakif user_msg:messages.append({"role": "user", "content": user_msg})if model_msg:messages.append({"role": "assistant", "content": model_msg})print("\n\n====conversation====\n", messages)print('debug: tokenizer.chat_template:\n{}'.format(tokenizer.chat_template))print('debug: tokenizer.default_chat_template:\n{}'.format(tokenizer.default_chat_template))model_inputs = tokenizer.apply_chat_template(messages,add_generation_prompt=True,tokenize=True,return_tensors="pt").to(next(model.parameters()).device)streamer = TextIteratorStreamer(tokenizer, timeout=600, skip_prompt=True, skip_special_tokens=True)generate_kwargs = {"input_ids": model_inputs,"streamer": streamer,"max_new_tokens": max_length,"do_sample": True,"top_p": top_p,"temperature": temperature,"stopping_criteria": StoppingCriteriaList([stop]),"repetition_penalty": 1.2,}t = Thread(target=model.generate, kwargs=generate_kwargs)t.start()for new_token in streamer:if new_token != '':history[-1][1] += new_tokenyield history