| | --- |
| | 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) |
| |
|
| |  |
| |
|
| | ```python |
| | 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("</s>") |
| | 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 |
| | ```shell |
| | #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 |
| | ``` |
| |
|
| | ```python |
| | 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":["</s>"], |
| | } |
| | chat_response = client.chat.completions.create( |
| | model="chatbot", |
| | messages=[ |
| | {"role": "user", "content": "你好"}, |
| | ], |
| | extra_body=call_args |
| | ) |
| | print("Chat response:", chat_response) |
| | |
| | |
| | ``` |
