开源模型应用落地-KnowLM模型小试-入门篇（一）

一、前言

? ? ?你是否了解知识图谱？如果了解，你们的业务场景是否应用了知识图谱？实际上，知识图谱在各行各业都被广泛使用。例如，在搜索引擎优化方面，通过利用知识图谱，搜索引擎可以更好地理解网页内容之间的关系，从而提高搜索结果的准确性和相关性。在医药领域，知识图谱可以整合医学知识和临床数据，辅助医生进行诊断和治疗决策，并提供个性化的医疗建议和健康管理服务。

? ? 接下来我们将进一步探讨如何利用开源的大型语言模型，以更好地进行知识提取。

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二、术语

? ? 2.1. 知识图谱

? ? 是一种结构化的知识表示方式，用于描述现实世界中的实体和它们之间的关系。它是一种用于组织和表示知识的图形模型，其中的实体和关系被表示为节点和边。

? ? 知识图谱使用的常见业务场景：

• 搜索引擎优化（SEO）：知识图谱可以帮助搜索引擎理解网页内容之间的关系，提高搜索结果的准确性和相关性。
• 问答系统：知识图谱可以用于构建智能问答系统，使用户能够通过提问获取准确的答案，而不仅仅是关键词匹配。
• 个性化推荐：知识图谱可以分析用户的兴趣、喜好和行为，并为用户提供个性化的推荐内容，例如商品推荐、新闻推荐等。
• 智能客服：知识图谱可以用于构建智能客服系统，通过理解用户问题和上下文，提供准确的答案或引导用户解决问题。
• 企业知识管理：知识图谱可以整合和管理企业内部的大量知识和信息，提供快速的搜索、浏览和分享功能，促进知识的共享和协作。
• 智能助理：知识图谱可以作为智能助理的核心，帮助用户管理日程安排、获取有用信息、执行任务等。
• 数据分析和决策支持：知识图谱可以用于整合和分析大量的结构化和非结构化数据，帮助企业做出更准确的决策和预测。
• 医疗健康：知识图谱可以整合医学知识和临床数据，辅助医生进行诊断和治疗决策，提供个性化的医疗建议和健康管理服务。
• 金融风控：知识图谱可以整合各种金融数据和信息，帮助金融机构进行风险评估、欺诈检测和信用评级等业务。
• 社交网络分析：知识图谱可以分析社交网络中的关系和行为模式，帮助企业和组织发现潜在的合作伙伴、影响者和市场机会。

? ? 2.2. 实体

? ? 是知识图谱中的核心概念，代表现实世界中的具体或抽象事物。实体可以是人、地点、事件、概念等等。每个实体在知识图谱中通常由一个唯一的标识符表示。例如，在一个包含音乐相关知识的图谱中，歌手、专辑、歌曲可以作为实体。

? ? 2.3. 关系

? ? 指的是实体之间的连接或相互作用。关系描述了实体之间的某种关联、依赖、属性等。关系可以是有向的或无向的，可以是单值的或多值的。例如，在一个社交网络的知识图谱中，"朋友"关系可以连接一个人实体和另一个人实体，描述他们之间的友谊关系。

? ? 2.4. KnowLM

? ? 是由浙江大学NLP&KG团队的在读博士生研发并开源的项目，是一种将LLM与知识图谱结合的知识抽取大模型，主要包含的任务有命名实体识别（NER）、事件抽取（EE）、关系抽取（RE）。github 地址：https://github.com/zjunlp/KnowLM/blob/main/README_ZH.md

这是KnowLM框架的总览图。主要有三个技术特色：

1.知识提示：基于知识图谱等结构化数据的知识提示生成和知识增强约束技术，解决知识抽取和推理问题

2.知识编辑：基于知识编辑技术对齐大模型内过时、错误及价值观不正确的知识，解决知识谬误问题 (英文版Tutorial)

3.知识交互：基于知识动态交互和反馈实现工具组合学习及多智能体协作，解决大模型具身认知问题 (英文版Tutorial)

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三、前置条件

3.1. 下载模型文件

? ? # 基础模型

? ??https://huggingface.co/zjunlp/knowlm-7b-base/tree/main

? ?

? ? # 对话模型

? ??https://huggingface.co/zjunlp/knowlm-7b-chat/tree/main

? ??

? ? PS：

? ? 1) 从huggingface中下载

? ? 2)?modelscope没有对应模型，需要下载其他版本，例如：git clone https://www.modelscope.cn/ZJUNLP/knowlm-13b-ie.git

3.2. windows操作系统（内存≥32GB）

3.3. 安装环境

? ? 1)?conda create -n knowlm python=3.10 -y

? ? 2)?conda activate knowlm

? ? 3)?pip install torch ?-i https://pypi.tuna.tsinghua.edu.cn/simple

? ? 4)?pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

? ? PS:

? ? 1）requirements.txt文件内容如下（要安装特定版本，需要指定版本号）：

? ? ? ? bitsandbytes
?? ??? ?datasets
?? ??? ?sentencepiece
?? ??? ?fire
?? ??? ?accelerate
?? ??? ?transformers
?? ??? ?peft

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四、技术实现

4.1. 加载tokenizer

tokenizer = LlamaTokenizer.from_pretrained(modelPath)

4.2.加载model

model = LlamaForCausalLM.from_pretrained(modelPath, device_map="cpu",low_cpu_mem_usage=True).eval()

4.3.推理（非流式输出）

generation_output = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=8192,
    )
s = generation_output.sequences[0]
output = tokenizer.decode(s)

4.4.完整代码

# -*-  coding = utf-8 -*-
import torch
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
import time

modelPath = "E:\\model\\knowlm-7b-chat"

response_split =  "### Response:"

def get_response(output: str) -> str:
    result = output.split(response_split)[1].strip()
    result = result.replace('</s>', '')
    return result

def generate(model,tokenizer,message,generation_config):
    s_format = "{'head':'', 'relation':'', 'tail':''}"
    instruction = f"""
        Please find the possible head entities (subjects) and tail entities (objects) in the text and give the corresponding relation triples. Please format your answer as a list of relation triples in the form of {s_format}.
         """
    prompt = generate_prompt_input(instruction,message)
    inputs = tokenizer(prompt, return_tensors="pt")

    if torch.cuda.is_available():
        input_ids = inputs["input_ids"].to('cuda')
    else:
        input_ids = inputs["input_ids"]

    generation_output = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=8192,
    )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
    return get_response(output)

# Generate the prompt for the input of LM model
def generate_prompt_no_input(instruction):
    return f"""
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}

### Response:
"""

def generate_prompt_input(instruction,input):
    return f"""
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{instruction}

### Input:
{input}

### Response:
"""


def loadTokenizer():
    tokenizer = LlamaTokenizer.from_pretrained(modelPath)
    return tokenizer

def loadModel():
    model = LlamaForCausalLM.from_pretrained(modelPath, device_map="cpu",low_cpu_mem_usage=True).eval()

    return model

if __name__ == '__main__':
    model = loadModel()
    tokenizer = loadTokenizer()
    start_time = time.time()

    generation_config = GenerationConfig.from_pretrained(modelPath, trust_remote_code=True)  # 可指定不同的生成长度、top_p等相关超参
    message = "Zhang San and I are good friends. This Friday, we went to visit a museum together"
    response = generate(model,tokenizer,message,generation_config)
    print(f'response: {response}')

    end_time = time.time()
    print("执行耗时: {:.2f}秒".format(end_time-start_time))

4.5.程序输出

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五、附带说明

5.1.?KnowLM项目自带的prompt模板

    relation_template =  {
		0:'已知候选的关系列表：{s_schema}，请你根据关系列表，从以下输入中抽取出可能存在的头实体与尾实体，并给出对应的关系三元组。请按照{s_format}的格式回答。',
		1:'我将给你个输入，请根据关系列表：{s_schema}，从输入中抽取出可能包含的关系三元组，并以{s_format}的形式回答。',
		2:'我希望你根据关系列表从给定的输入中抽取可能的关系三元组，并以{s_format}的格式回答，关系列表={s_schema}。',
		3:'给定的关系列表是：{s_schema}\n根据关系列表抽取关系三元组，在这个句子中可能包含哪些关系三元组？请以{s_format}的格式回答。',
	}

	relation_int_out_format = {
		0:['"(头实体,关系,尾实体)"', relation_convert_target0],
		2:['"关系：头实体,尾实体\n"', relation_convert_target2],
		3:["JSON字符串[{'head':'', 'relation':'', 'tail':''}, ]", relation_convert_target3],
	}


	en_relation_template = {
		0: 'Identify the head entities (subjects) and tail entities (objects) in the following text and provide the corresponding relation triples from relation list {s_schema}. Please provide your answer as a list of relation triples in the form of {s_format}.',
		1: 'Identify the subjects and objects in the text that are related, and provide the corresponding relation triples from relation {s_schema} in the format of {s_format}.',
		2: 'From the given text, extract the possible head entities (subjects) and tail entities (objects) and give the corresponding relation triples. The relations are {s_schema}. Please format your answer as a list of relation triples in the form of {s_format}.',
		3: 'Your task is to identify the head entities (subjects) and tail entities (objects) in the following text and extract the corresponding relation triples, the possible relation list is {s_schema}. Your answer should include relation triples, with each triple formatted as {s_format}.',
		4: 'Given the text, extract the possible head entities (subjects) and tail entities (objects) and provide the corresponding relation triples, the possible relation list is {s_schema}. Format your answer as a list of relation triples in the form of {s_format}.',
		5: 'Your goal is to identify the head entities (subjects) and tail entities (objects) in the text and give the corresponding relation triples. The given relation list is {s_schema}. Please answer with a list of relation triples in the form of {s_format}.',
		6: 'Please extract the possible head entities (subjects) and tail entities (objects) from the text and provide the corresponding relation triples from candidate relation list {s_schema}. Your answer should be in the form of a list of relation triples: {s_format}.',
		7: 'Your task is to extract the possible head entities (subjects) and tail entities (objects) in the given text and give the corresponding relation triples. The relations are {s_schema}. Please answer using the format of a list of relation triples: {s_format}.',
		8: 'Given the {s_schema}, identify the head entities (subjects) and tail entities (objects) and provide the corresponding relation triples. Your answer should consist of relation triples, with each triple formatted as {s_format}',
		9: 'Please find the possible head entities (subjects) and tail entities (objects) in the text based on the relation list {s_schema} and give the corresponding relation triples. Please format your answer as a list of relation triples in the form of {s_format}.',
		10: 'Given relation list {s_schema}, extract the possible subjects and objects from the text and give the corresponding relation triples in the format of {s_format}.',
		11: 'Extract the entities involved in the relationship described in the text and provide the corresponding triples in the format of {s_format}, the possible relation list is {s_schema}.',
		12: 'Given relation list {s_schema}, provide relation triples for the entities and their relationship in the text, using the format of {s_format}.',
		13: 'Extract the entities and their corresponding relationships from the given relationships are {s_schema} and provide the relation triples in the format of {s_format}.',
	}

	en_relation_int_out_format = {
		0: "{'head':'', 'relation':'', 'tail':''}",
		1: "(Subject, Relation, Object)",
		2: "[Subject, Relation, Object]",
		3: "{head, relation, tail}",
		4: "<head, relation, tail>",
	}