在阿里云上部署Llama3（中文版本）_llama3 chinese

1、代码和模型的下载，模型下载到代码根目录下

git clone https://github.com/CrazyBoyM/llama3-Chinese-chat --depth 1
git lfs clone https://www.modelscope.cn/baicai003/Llama3-Chinese_v2.git

2、环境搭建

1.新建conda环境

conda create -n llama3 python=3.10
conda activate llama3

2.新建requirements.txt文件，插入

torch
fairscale
fire
tiktoken==0.4.0
blobfile
transformers
peft

3.执行

pip install -r requirements.txt
pip install -U streamlit

3、运行

#/mnt/workspace/llama3-Chinese-chat/Llama3-Chinese_v2  为自己的实际路径，自己修改
 
streamlit run deploy/web_streamlit_for_instruct.py /mnt/workspace/llama3-Chinese-chat/Llama3-Chinese_v2 --theme.base="dark" --server.address=127.0.0.1

• fp16模式 大概占用16G显存，推荐24G显卡使用
• int4模式大概占用8G显存，推荐至少10G显存使用，需要自行修改搜索代码中load_in_4bit=True

4、最终推理

默认情况下直接运行以下代码即可体验llama3中文对话，请自行修改model_name_or_path为您下载的模型路径

from transformers import AutoTokenizer, AutoConfig, AddedToken, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
from dataclasses import dataclass
from typing import Dict
import torch
import copy
 
## 定义聊天模板
@dataclass
class Template:
    template_name:str
    system_format: str
    user_format: str
    assistant_format: str
    system: str
    stop_word: str
 
template_dict: Dict[str, Template] = dict()
 
def register_template(template_name, system_format, user_format, assistant_format, system, stop_word=None):
    template_dict[template_name] = Template(
        template_name=template_name,
        system_format=system_format,
        user_format=user_format,
        assistant_format=assistant_format,
        system=system,
        stop_word=stop_word,
    )
 
# 这里的系统提示词是训练时使用的，推理时可以自行尝试修改效果
register_template(
    template_name='llama3',
    system_format='<|begin_of_text|><<SYS>>\n{content}\n<</SYS>>\n\n',
    user_format='<|start_header_id|>user<|end_header_id|>\n\n{content}<|eot_id|>',
    assistant_format='<|start_header_id|>assistant<|end_header_id|>\n\n{content}<|end_of_text|>\n',
    system="You are a helpful, excellent and smart assistant. "
        "Please respond to the user using the language they input, ensuring the language is elegant and fluent."
        "If you don't know the answer to a question, please don't share false information.",
    stop_word='<|end_of_text|>'
)
 
 
## 加载模型
def load_model(model_name_or_path, load_in_4bit=False, adapter_name_or_path=None):
    if load_in_4bit:
        quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
            llm_int8_threshold=6.0,
            llm_int8_has_fp16_weight=False,
        )
    else:
        quantization_config = None
 
    # 加载base model
    model = AutoModelForCausalLM.from_pretrained(
        model_name_or_path,
        load_in_4bit=load_in_4bit,
        trust_remote_code=True,
        low_cpu_mem_usage=True,
        torch_dtype=torch.float16,
        device_map='auto',
        quantization_config=quantization_config
    )
 
    # 加载adapter
    if adapter_name_or_path is not None:
        model = PeftModel.from_pretrained(model, adapter_name_or_path)
 
    return model
 
## 加载tokenzier
def load_tokenizer(model_name_or_path):
    tokenizer = AutoTokenizer.from_pretrained(
        model_name_or_path,
        trust_remote_code=True,
        use_fast=False
    )
 
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
 
    return tokenizer
 
## 构建prompt
def build_prompt(tokenizer, template, query, history, system=None):
    template_name = template.template_name
    system_format = template.system_format
    user_format = template.user_format
    assistant_format = template.assistant_format
    system = system if system is not None else template.system
 
    history.append({"role": 'user', 'message': query})
    input_ids = []
 
    # 添加系统信息
    if system_format is not None:
        if system is not None:
            system_text = system_format.format(content=system)
            input_ids = tokenizer.encode(system_text, add_special_tokens=False)
    # 拼接历史对话
    for item in history:
        role, message = item['role'], item['message']
        if role == 'user':
            message = user_format.format(content=message, stop_token=tokenizer.eos_token)
        else:
            message = assistant_format.format(content=message, stop_token=tokenizer.eos_token)
        tokens = tokenizer.encode(message, add_special_tokens=False)
        input_ids += tokens
    input_ids = torch.tensor([input_ids], dtype=torch.long)
 
    return input_ids
 
 
def main():
    model_name_or_path = 'shareAI/llama3-Chinese-chat-8b' # 模型名称或路径，请修改这里
    template_name = 'llama3'
    adapter_name_or_path = None
 
    template = template_dict[template_name]
    # 若开启4bit推理能够节省很多显存，但效果可能下降
    load_in_4bit = False
 
    # 生成超参配置，可修改以取得更好的效果
    max_new_tokens = 500 # 每次回复时，AI生成文本的最大长度
    top_p = 0.9
    temperature = 0.6 # 越大越有创造性，越小越保守
    repetition_penalty = 1.1 # 越大越能避免吐字重复
 
    # 加载模型
    print(f'Loading model from: {model_name_or_path}')
    print(f'adapter_name_or_path: {adapter_name_or_path}')
    model = load_model(
        model_name_or_path,
        load_in_4bit=load_in_4bit,
        adapter_name_or_path=adapter_name_or_path
    ).eval()
    tokenizer = load_tokenizer(model_name_or_path if adapter_name_or_path is None else adapter_name_or_path)
    if template.stop_word is None:
        template.stop_word = tokenizer.eos_token
    stop_token_id = tokenizer.encode(template.stop_word, add_special_tokens=True)
    assert len(stop_token_id) == 1
    stop_token_id = stop_token_id[0]
 
    history = []
 
    query = input('# User：')
    while True:
        query = query.strip()
        input_ids = build_prompt(tokenizer, template, query, copy.deepcopy(history), system=None).to(model.device)
        outputs = model.generate(
            input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True,
            top_p=top_p, temperature=temperature, repetition_penalty=repetition_penalty,
            eos_token_id=stop_token_id
        )
        outputs = outputs.tolist()[0][len(input_ids[0]):]
        response = tokenizer.decode(outputs)
        response = response.strip().replace(template.stop_word, "").strip()
 
        # 存储对话历史
        history.append({"role": 'user', 'message': query})
        history.append({"role": 'assistant', 'message': response})
 
        # 当对话长度超过6轮时，清空最早的对话，可自行修改
        if len(history) > 12:
            history = history[:-12]
 
        print("# Llama3-Chinese：{}".format(response))
        query = input('# User：')
 
 
if __name__ == '__main__':
    main()