--- license: apache-2.0 --- ### This model is trained from Mistral-7B-Instruct-V0.2 with 90% chinese dataset and 10% english dataset github [Web-UI](https://github.com/moseshu/llama2-chat/tree/main/webui) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f4c7172f63f904a0c61ba3/JIeyxhTm9_PNzXyU7wQVd.png) ``` from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer,AutoTokenizer,AutoModelForCausalLM,MistralForCausalLM import torch model_id=Mistral-7B-Instruct-v0.4 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto",) chat_template="{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}" def chat_format(conversation:list): system_prompt = "You are a helpful, respectful and honest assistant.Help humman as much as you can." id = tokenizer.apply_chat_template(conversation,chat_template=chat_template,tokenize=False) return id user_chat=[{"role":"user","content":"你好,最近在干嘛呢"}] text = chat_format(user_chat).rstrip("") def predict(content_prompt): inputs = tokenizer(content_prompt,return_tensors="pt",add_special_tokens=True) input_ids = inputs["input_ids"].to("cuda:0") # print(f"input length:{len(input_ids[0])}") with torch.no_grad(): generation_output = model.generate( input_ids=input_ids, #generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=2048, top_p=0.9, num_beams=1, do_sample=True, repetition_penalty=1.0, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, ) s = generation_output.sequences[0] output = tokenizer.decode(s,skip_special_tokens=True) output1 = output.split("[/INST]")[-1].strip() # print(output1) return output1 predict(text) output:你好!作为一个大型语言模型,我一直在学习和提高自己的能力。最近,我一直在努力学习新知识、改进算法,以便更好地回答用户的问题并提供帮助。同时,我也会定期接受人工智能专家的指导和评估,以确保我的表现不断提升。希望这些信息对你有所帮助! ``` ## vllm server ``` llama2-chat-template.jinja file is chat-template above model_path=Mistral-7B-Instruct-V0.4 python -m vllm.entrypoints.openai.api_server --model=$model_path \ --trust-remote-code --host 0.0.0.0 --port 7777 \ --gpu-memory-utilization 0.8 \ --max-model-len 8192 --chat-template llama2-chat-template.jinja \ --tensor-parallel-size 1 --served-model-name chatbot ``` ``` from openai import OpenAI # Set OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://localhost:7777/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) call_args = { 'temperature': 0.7, 'top_p': 0.9, 'top_k': 40, 'max_tokens': 2048, # output-len 'presence_penalty': 1.0, 'frequency_penalty': 0.0, "repetition_penalty":1.0, "stop":[""], } chat_response = client.chat.completions.create( model="llama", messages=[ {"role": "user", "content": "你好"}, ], extra_body=call_args ) print("Chat response:", chat_response) ```