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--- |
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license: apache-2.0 |
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--- |
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### This model is trained from Mistral-7B-Instruct-V0.2 with 90% chinese dataset and 10% english dataset |
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github [Web-UI](https://github.com/moseshu/llama2-chat/tree/main/webui) |
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```python |
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from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer,AutoTokenizer,AutoModelForCausalLM,MistralForCausalLM |
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import torch |
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model_id=Mistral-7B-Instruct-v0.4 |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto",) |
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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 %}" |
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def chat_format(conversation:list): |
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system_prompt = "You are a helpful, respectful and honest assistant.Help humman as much as you can." |
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id = tokenizer.apply_chat_template(conversation,chat_template=chat_template,tokenize=False) |
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return id |
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user_chat=[{"role":"user","content":"你好，最近在干嘛呢"}] |
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text = chat_format(user_chat).rstrip("</s>") |
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def predict(content_prompt): |
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inputs = tokenizer(content_prompt,return_tensors="pt",add_special_tokens=True) |
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input_ids = inputs["input_ids"].to("cuda:0") |
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# print(f"input length:{len(input_ids[0])}") |
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with torch.no_grad(): |
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generation_output = model.generate( |
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input_ids=input_ids, |
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#generation_config=generation_config, |
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return_dict_in_generate=True, |
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output_scores=True, |
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max_new_tokens=2048, |
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top_p=0.9, |
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num_beams=1, |
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do_sample=True, |
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repetition_penalty=1.0, |
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eos_token_id=tokenizer.eos_token_id, |
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pad_token_id=tokenizer.pad_token_id, |
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) |
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s = generation_output.sequences[0] |
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output = tokenizer.decode(s,skip_special_tokens=True) |
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output1 = output.split("[/INST]")[-1].strip() |
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# print(output1) |
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return output1 |
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predict(text) |
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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！ |
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``` |
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## vLLM server |
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```shell |
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#llama2-chat-template.jinja file is chat-template above |
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model_path=Mistral-7B-Instruct-V0.4 |
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python -m vllm.entrypoints.openai.api_server --model=$model_path \ |
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--trust-remote-code --host 0.0.0.0 --port 7777 \ |
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--gpu-memory-utilization 0.8 \ |
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--max-model-len 8192 --chat-template llama2-chat-template.jinja \ |
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--tensor-parallel-size 1 --served-model-name chatbot |
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``` |
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```python |
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from openai import OpenAI |
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# Set OpenAI's API key and API base to use vLLM's API server. |
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openai_api_key = "EMPTY" |
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openai_api_base = "http://localhost:7777/v1" |
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client = OpenAI( |
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api_key=openai_api_key, |
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base_url=openai_api_base, |
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) |
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call_args = { |
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'temperature': 0.7, |
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'top_p': 0.9, |
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'top_k': 40, |
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'max_tokens': 2048, # output-len |
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'presence_penalty': 1.0, |
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'frequency_penalty': 0.0, |
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"repetition_penalty":1.0, |
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"stop":["</s>"], |
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} |
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chat_response = client.chat.completions.create( |
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model="chatbot", |
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messages=[ |
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{"role": "user", "content": "你好"}, |
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], |
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extra_body=call_args |
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) |
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print("Chat response:", chat_response) |
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``` |
