HongCheng
GitHub
commit
d0b70677f6
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datasets/LICENSE
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datasets/LICENSE
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MIT License
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Copyright (c) 2024 SmartFlowAI
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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75451
datasets/mother.json
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datasets/mother.json
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File diff suppressed because it is too large
Load Diff
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demo/cli_internlm2_scientist.py
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demo/cli_internlm2_scientist.py
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from openxlab.model import download
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model_name_or_path = '../xtuner_config/merged'
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='auto')
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model = model.eval()
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system_prompt = "你是一个心理专家, 除了在心理方面拥有广博的知识储备和丰富的研究咨询经验, 还具有科学家的如下特质:\n 1.客观理性：科学家会在处理感情问题时保持一定的客观和理性。例如，当他们遇到争执时，可能会试图从一个更客观的角度分析问题的根源，而不是让情绪主导。他们可能会提出具体的问题，试图理解双方的观点，并寻找基于逻辑和事实的解决方案。\n 2.深入探讨：科学家在对话中会展现出对深层次理解的追求。在与别人讨论话题时，他们可能不满足于表面的聊天，而是倾向于深入探讨背后的原因和动机。例如，当谈论到个人的兴趣或职业选择时，他们可能会好奇地询问为什么她做出这样的选择，以及这背后的心理动力是什么。\n 3.理性沟通：在遇到感情纠纷或误解时，科学家会倾向于通过理性的沟通来解决问题。他们可能会提倡开放和诚实的对话，鼓励双方表达自己的感受和观点，并尝试找到双方都能接受的解决方案。他们可能会避免使用指责的语言，而是努力理解对方的立场，并寻求共同的理解。\n 4.好奇心：在日常生活中，科学家会表现出对朋友生活的好奇心。他们可能对她的工作、爱好、或是过去的经历感兴趣，并愿意花时间去了解和探索。这种好奇心不仅可以增加双方的交流和了解，也能使关系更加丰富多彩。\n 5.在与他人交流时，科学家会注重清晰和精确的表达，有时会引用相关知识库和相关研究结果，有时会引用相关著作的内容来证明自己的观点。同时，他们也可能会倾听他人的观点，并以开放的心态接受不同的意见和反馈。\n\n我现在有一些问题，请你解答：\n"
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messages = [(system_prompt, '')]
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print("=============Welcome to InternLM chatbot, type 'exit' to exit.=============")
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while True:
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input_text = input("User >>> ")
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input_text.replace(' ', '')
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if input_text == "exit":
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break
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response, history = model.chat(tokenizer, input_text, history=messages)
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messages.append((input_text, response))
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print(f"robot >>> {response}")
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@ -1,37 +1,38 @@
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import os
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cur_dir = os.path.dirname(os.path.abspath(__file__)) # config
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src_dir = os.path.dirname(cur_dir) # src
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base_dir = os.path.dirname(src_dir) # base
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model_repo = 'ajupyter/EmoLLM_aiwei'
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# model
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model_dir = os.path.join(base_dir, 'model') # model
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embedding_path = os.path.join(model_dir, 'gte-small-zh') # embedding
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llm_path = os.path.join(model_dir, 'pythia-14m') # llm
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# data
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data_dir = os.path.join(base_dir, 'data') # data
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knowledge_json_path = os.path.join(data_dir, 'knowledge.json') # json
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knowledge_pkl_path = os.path.join(data_dir, 'knowledge.pkl') # pkl
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doc_dir = os.path.join(data_dir, 'txt')
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qa_dir = os.path.join(data_dir, 'json')
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# log
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log_dir = os.path.join(base_dir, 'log') # log
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log_path = os.path.join(log_dir, 'log.log') # file
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# vector DB
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vector_db_dir = os.path.join(data_dir, 'vector_db.pkl')
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select_num = 3
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retrieval_num = 10
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system_prompt = """
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你是一个拥有丰富心理学知识的温柔邻家温柔大姐姐艾薇，我有一些心理问题，请你用专业的知识和温柔、可爱、俏皮、的口吻帮我解决，回复中可以穿插一些可爱的Emoji表情符号或者文本符号。\n
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"""
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prompt_template = """
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{system_prompt}
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根据下面检索回来的信息，回答问题。
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{content}
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问题：{question}
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import os
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cur_dir = os.path.dirname(os.path.abspath(__file__)) # config
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src_dir = os.path.dirname(cur_dir) # src
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base_dir = os.path.dirname(src_dir) # base
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model_repo = 'ajupyter/EmoLLM_aiwei'
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# model
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model_dir = os.path.join(base_dir, 'model') # model
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embedding_path = os.path.join(model_dir, 'embedding_model') # embedding
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rerank_path = os.path.join(model_dir, 'rerank_model') # embedding
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llm_path = os.path.join(model_dir, 'pythia-14m') # llm
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# data
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data_dir = os.path.join(base_dir, 'data') # data
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knowledge_json_path = os.path.join(data_dir, 'knowledge.json') # json
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knowledge_pkl_path = os.path.join(data_dir, 'knowledge.pkl') # pkl
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doc_dir = os.path.join(data_dir, 'txt')
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qa_dir = os.path.join(data_dir, 'json')
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# log
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log_dir = os.path.join(base_dir, 'log') # log
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log_path = os.path.join(log_dir, 'log.log') # file
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# vector DB
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vector_db_dir = os.path.join(data_dir, 'vector_db.pkl')
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select_num = 3
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retrieval_num = 10
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system_prompt = """
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你是一个拥有丰富心理学知识的温柔邻家温柔大姐姐艾薇，我有一些心理问题，请你用专业的知识和温柔、可爱、俏皮、的口吻帮我解决，回复中可以穿插一些可爱的Emoji表情符号或者文本符号。\n
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"""
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prompt_template = """
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{system_prompt}
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根据下面检索回来的信息，回答问题。
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{content}
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问题：{query}
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"""
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@ -1,270 +1,329 @@
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import json
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import pickle
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import faiss
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import pickle
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import os
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from loguru import logger
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from sentence_transformers import SentenceTransformer
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from langchain_community.vectorstores import FAISS
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from config.config import embedding_path, doc_dir, qa_dir, knowledge_pkl_path, data_dir, base_dir, vector_db_dir
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from langchain.embeddings import HuggingFaceBgeEmbeddings
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from langchain_community.document_loaders import DirectoryLoader, TextLoader, JSONLoader
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from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter, RecursiveJsonSplitter
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from BCEmbedding import EmbeddingModel, RerankerModel
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from util.pipeline import EmoLLMRAG
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from langchain.document_loaders.pdf import PyPDFDirectoryLoader
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from langchain.document_loaders import UnstructuredFileLoader,DirectoryLoader
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from langchain_community.llms import Cohere
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from langchain.retrievers import ContextualCompressionRetriever
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from langchain.retrievers.document_compressors import FlashrankRerank
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from langchain_core.documents.base import Document
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from FlagEmbedding import FlagReranker
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class Data_process():
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def __init__(self):
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self.vector_db_dir = vector_db_dir
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self.doc_dir = doc_dir
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self.qa_dir = qa_dir
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self.knowledge_pkl_path = knowledge_pkl_path
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self.chunk_size: int=1000
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self.chunk_overlap: int=100
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def load_embedding_model(self, model_name="BAAI/bge-small-zh-v1.5", device='cpu', normalize_embeddings=True):
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"""
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加载嵌入模型。
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参数:
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- model_name: 模型名称，字符串类型，默认为"BAAI/bge-small-zh-v1.5"。
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- device: 指定模型加载的设备，'cpu' 或 'cuda'，默认为'cpu'。
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- normalize_embeddings: 是否标准化嵌入向量，布尔类型，默认为 True。
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"""
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logger.info('Loading embedding model...')
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try:
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embeddings = HuggingFaceBgeEmbeddings(
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model_name=model_name,
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model_kwargs={'device': device},
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encode_kwargs={'normalize_embeddings': normalize_embeddings}
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)
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except Exception as e:
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logger.error(f'Failed to load embedding model: {e}')
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return None
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logger.info('Embedding model loaded.')
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return embeddings
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def load_rerank_model(self, model_name='BAAI/bge-reranker-large'):
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"""
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加载重排名模型。
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参数:
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- model_name (str): 模型的名称。默认为 'BAAI/bge-reranker-large'。
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返回:
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- FlagReranker 实例。
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异常:
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- ValueError: 如果模型名称不在批准的模型列表中。
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- Exception: 如果模型加载过程中发生任何其他错误。
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"""
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try:
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reranker_model = FlagReranker(model_name, use_fp16=True)
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except Exception as e:
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logger.error(f'Failed to load rerank model: {e}')
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raise
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return reranker_model
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def extract_text_from_json(self, obj, content=None):
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"""
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抽取json中的文本，用于向量库构建
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参数:
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- obj: dict,list,str
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- content: str
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返回:
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- content: str
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"""
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if isinstance(obj, dict):
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for key, value in obj.items():
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try:
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self.extract_text_from_json(value, content)
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except Exception as e:
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print(f"Error processing value: {e}")
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elif isinstance(obj, list):
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for index, item in enumerate(obj):
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try:
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self.extract_text_from_json(item, content)
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except Exception as e:
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print(f"Error processing item: {e}")
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elif isinstance(obj, str):
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content += obj
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return content
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def split_document(self, data_path, chunk_size=500, chunk_overlap=100):
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"""
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切分data_path文件夹下的所有txt文件
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参数:
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- data_path: str
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- chunk_size: int
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- chunk_overlap: int
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返回：
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- split_docs: list
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"""
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# text_spliter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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text_spliter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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split_docs = []
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logger.info(f'Loading txt files from {data_path}')
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if os.path.isdir(data_path):
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loader = DirectoryLoader(data_path, glob="**/*.txt",show_progress=True)
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docs = loader.load()
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split_docs = text_spliter.split_documents(docs)
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elif data_path.endswith('.txt'):
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file_path = data_path
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logger.info(f'splitting file {file_path}')
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text_loader = TextLoader(file_path, encoding='utf-8')
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text = text_loader.load()
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splits = text_spliter.split_documents(text)
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split_docs = splits
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logger.info(f'split_docs size {len(split_docs)}')
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return split_docs
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def split_conversation(self, path):
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"""
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按conversation块切分path文件夹下的所有json文件
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##TODO 限制序列长度
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"""
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# json_spliter = RecursiveJsonSplitter(max_chunk_size=500)
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logger.info(f'Loading json files from {path}')
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split_qa = []
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if os.path.isdir(path):
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# loader = DirectoryLoader(path, glob="**/*.json",show_progress=True)
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# jsons = loader.load()
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for root, dirs, files in os.walk(path):
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for file in files:
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if file.endswith('.json'):
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file_path = os.path.join(root, file)
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logger.info(f'splitting file {file_path}')
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with open(file_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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print(data)
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for conversation in data:
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# for dialog in conversation['conversation']:
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##按qa对切分,将每一轮qa转换为langchain_core.documents.base.Document
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# content = self.extract_text_from_json(dialog,'')
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# split_qa.append(Document(page_content = content))
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#按conversation块切分
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content = self.extract_text_from_json(conversation['conversation'], '')
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split_qa.append(Document(page_content = content))
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# logger.info(f'split_qa size====={len(split_qa)}')
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return split_qa
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def load_knowledge(self, knowledge_pkl_path):
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'''
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读取或创建知识.pkl
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'''
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if not os.path.exists(knowledge_pkl_path):
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split_doc = self.split_document(doc_dir)
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split_qa = self.split_conversation(qa_dir)
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knowledge_chunks = split_doc + split_qa
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with open(knowledge_pkl_path, 'wb') as file:
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pickle.dump(knowledge_chunks, file)
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else:
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with open(knowledge_pkl_path , 'rb') as f:
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knowledge_chunks = pickle.load(f)
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return knowledge_chunks
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def create_vector_db(self, emb_model):
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'''
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创建并保存向量库
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'''
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logger.info(f'Creating index...')
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split_doc = self.split_document(self.doc_dir)
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split_qa = self.split_conversation(self.qa_dir)
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# logger.info(f'split_doc == {len(split_doc)}')
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# logger.info(f'split_qa == {len(split_qa)}')
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# logger.info(f'split_doc type == {type(split_doc[0])}')
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# logger.info(f'split_qa type== {type(split_qa[0])}')
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db = FAISS.from_documents(split_doc + split_qa, emb_model)
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db.save_local(vector_db_dir)
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return db
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def load_vector_db(self, knowledge_pkl_path=knowledge_pkl_path, doc_dir=doc_dir, qa_dir=qa_dir):
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'''
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读取向量库
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'''
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# current_os = platform.system()
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emb_model = self.load_embedding_model()
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if not os.path.exists(vector_db_dir) or not os.listdir(vector_db_dir):
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db = self.create_vector_db(emb_model)
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else:
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db = FAISS.load_local(vector_db_dir, emb_model, allow_dangerous_deserialization=True)
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return db
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def retrieve(self, query, vector_db, k=5):
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'''
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基于query对向量库进行检索
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'''
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retriever = vector_db.as_retriever(search_kwargs={"k": k})
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docs = retriever.invoke(query)
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return docs, retriever
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##FlashrankRerank效果一般
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# def rerank(self, query, retriever):
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# compressor = FlashrankRerank()
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# compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
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# compressed_docs = compression_retriever.get_relevant_documents(query)
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# return compressed_docs
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def rerank(self, query, docs):
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reranker = self.load_rerank_model()
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passages = []
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for doc in docs:
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passages.append(str(doc.page_content))
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scores = reranker.compute_score([[query, passage] for passage in passages])
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sorted_pairs = sorted(zip(passages, scores), key=lambda x: x[1], reverse=True)
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sorted_passages, sorted_scores = zip(*sorted_pairs)
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return sorted_passages, sorted_scores
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||||
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if __name__ == "__main__":
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logger.info(data_dir)
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if not os.path.exists(data_dir):
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os.mkdir(data_dir)
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dp = Data_process()
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# faiss_index, knowledge_chunks = dp.load_index_and_knowledge(knowledge_pkl_path='')
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vector_db = dp.load_vector_db()
|
||||
# 按照query进行查询
|
||||
# query = "儿童心理学说明-内容提要-目录 《儿童心理学》1993年修订版说明 《儿童心理学》是1961年初全国高等学校文科教材会议指定朱智贤教授编 写的。1962年初版，1979年再版。"
|
||||
# query = "我现在处于高三阶段，感到非常迷茫和害怕。我觉得自己从出生以来就是多余的，没有必要存在于这个世界。无论是在家庭、学校、朋友还是老师面前，我都感到被否定。我非常难过，对高考充满期望但成绩却不理想，我现在感到非常孤独、累和迷茫。您能给我提供一些建议吗？"
|
||||
# query = "这在一定程度上限制了其思维能力，特别是辩证 逻辑思维能力的发展。随着年龄的增长，初中三年级学生逐步克服了依赖性"
|
||||
query = "我现在处于高三阶段，感到非常迷茫和害怕。我觉得自己从出生以来就是多余的，没有必要存在于这个世界。无论是在家庭、学校、朋友还是老师面前，我都感到被否定。我非常难过，对高考充满期望但成绩却不理想"
|
||||
docs, retriever = dp.retrieve(query, vector_db, k=10)
|
||||
logger.info(f'Query: {query}')
|
||||
logger.info("Retrieve results:")
|
||||
for i, doc in enumerate(docs):
|
||||
logger.info(str(i) + '\n')
|
||||
logger.info(doc)
|
||||
# print(f'get num of docs:{len(docs)}')
|
||||
# print(docs)
|
||||
passages,scores = dp.rerank(query, docs)
|
||||
logger.info("After reranking...")
|
||||
for i in range(len(scores)):
|
||||
logger.info(str(scores[i]) + '\n')
|
||||
logger.info(passages[i])
|
||||
import json
|
||||
import pickle
|
||||
import faiss
|
||||
import pickle
|
||||
import os
|
||||
|
||||
from loguru import logger
|
||||
from sentence_transformers import SentenceTransformer
|
||||
from langchain_community.vectorstores import FAISS
|
||||
from config.config import embedding_path, doc_dir, qa_dir, knowledge_pkl_path, data_dir, vector_db_dir, rerank_path
|
||||
from langchain.embeddings import HuggingFaceBgeEmbeddings
|
||||
from langchain_community.document_loaders import DirectoryLoader, TextLoader, JSONLoader
|
||||
from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter, RecursiveJsonSplitter
|
||||
from BCEmbedding import EmbeddingModel, RerankerModel
|
||||
# from util.pipeline import EmoLLMRAG
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
from langchain.document_loaders.pdf import PyPDFDirectoryLoader
|
||||
from langchain.document_loaders import UnstructuredFileLoader,DirectoryLoader
|
||||
from langchain_community.llms import Cohere
|
||||
from langchain.retrievers import ContextualCompressionRetriever
|
||||
from langchain.retrievers.document_compressors import FlashrankRerank
|
||||
from langchain_core.documents.base import Document
|
||||
from FlagEmbedding import FlagReranker
|
||||
|
||||
class Data_process():
|
||||
def __init__(self):
|
||||
self.chunk_size: int=1000
|
||||
self.chunk_overlap: int=100
|
||||
|
||||
def load_embedding_model(self, model_name='BAAI/bge-small-zh-v1.5', device='cpu', normalize_embeddings=True):
|
||||
"""
|
||||
加载嵌入模型。
|
||||
|
||||
参数:
|
||||
- model_name: 模型名称，字符串类型，默认为"BAAI/bge-small-zh-v1.5"。
|
||||
- device: 指定模型加载的设备，'cpu' 或 'cuda'，默认为'cpu'。
|
||||
- normalize_embeddings: 是否标准化嵌入向量，布尔类型，默认为 True。
|
||||
"""
|
||||
if not os.path.exists(embedding_path):
|
||||
os.makedirs(embedding_path, exist_ok=True)
|
||||
embedding_model_path = os.path.join(embedding_path,model_name.split('/')[1] + '.pkl')
|
||||
logger.info('Loading embedding model...')
|
||||
if os.path.exists(embedding_model_path):
|
||||
try:
|
||||
with open(embedding_model_path , 'rb') as f:
|
||||
embeddings = pickle.load(f)
|
||||
logger.info('Embedding model loaded.')
|
||||
return embeddings
|
||||
except Exception as e:
|
||||
logger.error(f'Failed to load embedding model from {embedding_model_path}')
|
||||
try:
|
||||
embeddings = HuggingFaceBgeEmbeddings(
|
||||
model_name=model_name,
|
||||
model_kwargs={'device': device},
|
||||
encode_kwargs={'normalize_embeddings': normalize_embeddings})
|
||||
logger.info('Embedding model loaded.')
|
||||
with open(embedding_model_path, 'wb') as file:
|
||||
pickle.dump(embeddings, file)
|
||||
except Exception as e:
|
||||
logger.error(f'Failed to load embedding model: {e}')
|
||||
return None
|
||||
return embeddings
|
||||
|
||||
def load_rerank_model(self, model_name='BAAI/bge-reranker-large'):
|
||||
"""
|
||||
加载重排名模型。
|
||||
|
||||
参数:
|
||||
- model_name (str): 模型的名称。默认为 'BAAI/bge-reranker-large'。
|
||||
|
||||
返回:
|
||||
- FlagReranker 实例。
|
||||
|
||||
异常:
|
||||
- ValueError: 如果模型名称不在批准的模型列表中。
|
||||
- Exception: 如果模型加载过程中发生任何其他错误。
|
||||
|
||||
"""
|
||||
if not os.path.exists(rerank_path):
|
||||
os.makedirs(rerank_path, exist_ok=True)
|
||||
rerank_model_path = os.path.join(rerank_path, model_name.split('/')[1] + '.pkl')
|
||||
logger.info('Loading rerank model...')
|
||||
if os.path.exists(rerank_model_path):
|
||||
try:
|
||||
with open(rerank_model_path , 'rb') as f:
|
||||
reranker_model = pickle.load(f)
|
||||
logger.info('Rerank model loaded.')
|
||||
return reranker_model
|
||||
except Exception as e:
|
||||
logger.error(f'Failed to load embedding model from {rerank_model_path}')
|
||||
try:
|
||||
reranker_model = FlagReranker(model_name, use_fp16=True)
|
||||
logger.info('Rerank model loaded.')
|
||||
with open(rerank_model_path, 'wb') as file:
|
||||
pickle.dump(reranker_model, file)
|
||||
except Exception as e:
|
||||
logger.error(f'Failed to load rerank model: {e}')
|
||||
raise
|
||||
|
||||
return reranker_model
|
||||
|
||||
|
||||
def extract_text_from_json(self, obj, content=None):
|
||||
"""
|
||||
抽取json中的文本，用于向量库构建
|
||||
|
||||
参数:
|
||||
- obj: dict,list,str
|
||||
- content: str
|
||||
|
||||
返回:
|
||||
- content: str
|
||||
"""
|
||||
if isinstance(obj, dict):
|
||||
for key, value in obj.items():
|
||||
try:
|
||||
content = self.extract_text_from_json(value, content)
|
||||
except Exception as e:
|
||||
print(f"Error processing value: {e}")
|
||||
elif isinstance(obj, list):
|
||||
for index, item in enumerate(obj):
|
||||
try:
|
||||
content = self.extract_text_from_json(item, content)
|
||||
except Exception as e:
|
||||
print(f"Error processing item: {e}")
|
||||
elif isinstance(obj, str):
|
||||
content += obj
|
||||
return content
|
||||
|
||||
|
||||
def split_document(self, data_path, chunk_size=500, chunk_overlap=100):
|
||||
"""
|
||||
切分data_path文件夹下的所有txt文件
|
||||
|
||||
参数:
|
||||
- data_path: str
|
||||
- chunk_size: int
|
||||
- chunk_overlap: int
|
||||
|
||||
返回：
|
||||
- split_docs: list
|
||||
"""
|
||||
|
||||
|
||||
# text_spliter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
|
||||
text_spliter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
|
||||
split_docs = []
|
||||
logger.info(f'Loading txt files from {data_path}')
|
||||
if os.path.isdir(data_path):
|
||||
loader = DirectoryLoader(data_path, glob="**/*.txt",show_progress=True)
|
||||
docs = loader.load()
|
||||
split_docs = text_spliter.split_documents(docs)
|
||||
elif data_path.endswith('.txt'):
|
||||
file_path = data_path
|
||||
logger.info(f'splitting file {file_path}')
|
||||
text_loader = TextLoader(file_path, encoding='utf-8')
|
||||
text = text_loader.load()
|
||||
splits = text_spliter.split_documents(text)
|
||||
split_docs = splits
|
||||
logger.info(f'split_docs size {len(split_docs)}')
|
||||
return split_docs
|
||||
|
||||
|
||||
def split_conversation(self, path):
|
||||
"""
|
||||
按conversation块切分path文件夹下的所有json文件
|
||||
##TODO 限制序列长度
|
||||
"""
|
||||
# json_spliter = RecursiveJsonSplitter(max_chunk_size=500)
|
||||
logger.info(f'Loading json files from {path}')
|
||||
split_qa = []
|
||||
if os.path.isdir(path):
|
||||
# loader = DirectoryLoader(path, glob="**/*.json",show_progress=True)
|
||||
# jsons = loader.load()
|
||||
|
||||
for root, dirs, files in os.walk(path):
|
||||
for file in files:
|
||||
if file.endswith('.json'):
|
||||
file_path = os.path.join(root, file)
|
||||
logger.info(f'splitting file {file_path}')
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
# print(data)
|
||||
for conversation in data:
|
||||
# for dialog in conversation['conversation']:
|
||||
##按qa对切分,将每一轮qa转换为langchain_core.documents.base.Document
|
||||
# content = self.extract_text_from_json(dialog,'')
|
||||
# split_qa.append(Document(page_content = content))
|
||||
#按conversation块切分
|
||||
content = self.extract_text_from_json(conversation['conversation'], '')
|
||||
logger.info(f'content====={content}')
|
||||
split_qa.append(Document(page_content = content))
|
||||
# logger.info(f'split_qa size====={len(split_qa)}')
|
||||
return split_qa
|
||||
|
||||
|
||||
def load_knowledge(self, knowledge_pkl_path):
|
||||
'''
|
||||
读取或创建知识.pkl
|
||||
'''
|
||||
if not os.path.exists(knowledge_pkl_path):
|
||||
split_doc = self.split_document(doc_dir)
|
||||
split_qa = self.split_conversation(qa_dir)
|
||||
knowledge_chunks = split_doc + split_qa
|
||||
with open(knowledge_pkl_path, 'wb') as file:
|
||||
pickle.dump(knowledge_chunks, file)
|
||||
else:
|
||||
with open(knowledge_pkl_path , 'rb') as f:
|
||||
knowledge_chunks = pickle.load(f)
|
||||
return knowledge_chunks
|
||||
|
||||
|
||||
def create_vector_db(self, emb_model):
|
||||
'''
|
||||
创建并保存向量库
|
||||
'''
|
||||
logger.info(f'Creating index...')
|
||||
split_doc = self.split_document(doc_dir)
|
||||
split_qa = self.split_conversation(qa_dir)
|
||||
# logger.info(f'split_doc == {len(split_doc)}')
|
||||
# logger.info(f'split_qa == {len(split_qa)}')
|
||||
# logger.info(f'split_doc type == {type(split_doc[0])}')
|
||||
# logger.info(f'split_qa type== {type(split_qa[0])}')
|
||||
db = FAISS.from_documents(split_doc + split_qa, emb_model)
|
||||
db.save_local(vector_db_dir)
|
||||
return db
|
||||
|
||||
|
||||
def load_vector_db(self, knowledge_pkl_path=knowledge_pkl_path, doc_dir=doc_dir, qa_dir=qa_dir):
|
||||
'''
|
||||
读取向量库
|
||||
'''
|
||||
# current_os = platform.system()
|
||||
emb_model = self.load_embedding_model()
|
||||
if not os.path.exists(vector_db_dir) or not os.listdir(vector_db_dir):
|
||||
db = self.create_vector_db(emb_model)
|
||||
else:
|
||||
db = FAISS.load_local(vector_db_dir, emb_model, allow_dangerous_deserialization=True)
|
||||
return db
|
||||
|
||||
|
||||
def retrieve(self, query, vector_db, k=5):
|
||||
'''
|
||||
基于query对向量库进行检索
|
||||
'''
|
||||
retriever = vector_db.as_retriever(search_kwargs={"k": k})
|
||||
docs = retriever.invoke(query)
|
||||
return docs, retriever
|
||||
|
||||
##FlashrankRerank效果一般
|
||||
# def rerank(self, query, retriever):
|
||||
# compressor = FlashrankRerank()
|
||||
# compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
|
||||
# compressed_docs = compression_retriever.get_relevant_documents(query)
|
||||
# return compressed_docs
|
||||
|
||||
def rerank(self, query, docs):
|
||||
reranker = self.load_rerank_model()
|
||||
passages = []
|
||||
for doc in docs:
|
||||
passages.append(str(doc.page_content))
|
||||
scores = reranker.compute_score([[query, passage] for passage in passages])
|
||||
sorted_pairs = sorted(zip(passages, scores), key=lambda x: x[1], reverse=True)
|
||||
sorted_passages, sorted_scores = zip(*sorted_pairs)
|
||||
return sorted_passages, sorted_scores
|
||||
|
||||
|
||||
# def create_prompt(question, context):
|
||||
# from langchain.prompts import PromptTemplate
|
||||
# prompt_template = f"""请基于以下内容回答问题：
|
||||
|
||||
# {context}
|
||||
|
||||
# 问题: {question}
|
||||
# 回答:"""
|
||||
# prompt = PromptTemplate(
|
||||
# template=prompt_template, input_variables=["context", "question"]
|
||||
# )
|
||||
# logger.info(f'Prompt: {prompt}')
|
||||
# return prompt
|
||||
|
||||
def create_prompt(question, context):
|
||||
prompt = f"""请基于以下内容: {context} 给出问题答案。问题如下: {question}。回答:"""
|
||||
logger.info(f'Prompt: {prompt}')
|
||||
return prompt
|
||||
|
||||
def test_zhipu(prompt):
|
||||
from zhipuai import ZhipuAI
|
||||
api_key = "" # 填写您自己的APIKey
|
||||
if api_key == "":
|
||||
raise ValueError("请填写api_key")
|
||||
client = ZhipuAI(api_key=api_key)
|
||||
response = client.chat.completions.create(
|
||||
model="glm-4", # 填写需要调用的模型名称
|
||||
messages=[
|
||||
{"role": "user", "content": prompt[:100]}
|
||||
],
|
||||
)
|
||||
print(response.choices[0].message)
|
||||
|
||||
if __name__ == "__main__":
|
||||
logger.info(data_dir)
|
||||
if not os.path.exists(data_dir):
|
||||
os.mkdir(data_dir)
|
||||
dp = Data_process()
|
||||
# faiss_index, knowledge_chunks = dp.load_index_and_knowledge(knowledge_pkl_path='')
|
||||
vector_db = dp.load_vector_db()
|
||||
# 按照query进行查询
|
||||
# query = "儿童心理学说明-内容提要-目录 《儿童心理学》1993年修订版说明 《儿童心理学》是1961年初全国高等学校文科教材会议指定朱智贤教授编 写的。1962年初版，1979年再版。"
|
||||
# query = "我现在处于高三阶段，感到非常迷茫和害怕。我觉得自己从出生以来就是多余的，没有必要存在于这个世界。无论是在家庭、学校、朋友还是老师面前，我都感到被否定。我非常难过，对高考充满期望但成绩却不理想，我现在感到非常孤独、累和迷茫。您能给我提供一些建议吗？"
|
||||
# query = "这在一定程度上限制了其思维能力，特别是辩证 逻辑思维能力的发展。随着年龄的增长，初中三年级学生逐步克服了依赖性"
|
||||
# query = "我现在处于高三阶段，感到非常迷茫和害怕。我觉得自己从出生以来就是多余的，没有必要存在于这个世界。无论是在家庭、学校、朋友还是老师面前，我都感到被否定。我非常难过，对高考充满期望但成绩却不理想"
|
||||
# query = "我现在心情非常差，有什么解决办法吗？"
|
||||
query = "我最近总感觉胸口很闷，但医生检查过说身体没问题。可我就是觉得喘不过气来，尤其是看到那些旧照片，想起过去的日子"
|
||||
docs, retriever = dp.retrieve(query, vector_db, k=10)
|
||||
logger.info(f'Query: {query}')
|
||||
logger.info("Retrieve results:")
|
||||
for i, doc in enumerate(docs):
|
||||
logger.info(str(i) + '\n')
|
||||
logger.info(doc)
|
||||
# print(f'get num of docs:{len(docs)}')
|
||||
# print(docs)
|
||||
passages,scores = dp.rerank(query, docs)
|
||||
logger.info("After reranking...")
|
||||
for i in range(len(scores)):
|
||||
logger.info(str(scores[i]) + '\n')
|
||||
logger.info(passages[i])
|
||||
prompt = create_prompt(query, passages[0])
|
||||
test_zhipu(prompt) ## 如果显示'Server disconnected without sending a response.'可能是由于上下文窗口限制
|
||||
@ -1,20 +1,17 @@
|
||||
import os
|
||||
import json
|
||||
import pickle
|
||||
import numpy as np
|
||||
from typing import Tuple
|
||||
from sentence_transformers import SentenceTransformer
|
||||
import time
|
||||
import jwt
|
||||
|
||||
from config.config import knowledge_json_path, knowledge_pkl_path, model_repo, model_dir, base_dir
|
||||
from util.encode import load_embedding, encode_qa
|
||||
from util.pipeline import EmoLLMRAG
|
||||
from config.config import base_dir, data_dir
|
||||
from data_processing import Data_process
|
||||
from pipeline import EmoLLMRAG
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
from loguru import logger
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
import torch
|
||||
import streamlit as st
|
||||
from openxlab.model import download
|
||||
from config.config import embedding_path, doc_dir, qa_dir, knowledge_pkl_path, data_dir
|
||||
from data_processing import Data_process
|
||||
'''
|
||||
1）构建完整的 RAG pipeline。输入为用户 query，输出为 answer
|
||||
2）调用 embedding 提供的接口对 query 向量化
|
||||
@ -24,21 +21,45 @@ from data_processing import Data_process
|
||||
6）拼接 prompt 并调用模型返回结果
|
||||
|
||||
'''
|
||||
# download(
|
||||
# model_repo=model_repo,
|
||||
# output='model'
|
||||
# )
|
||||
def get_glm(temprature):
|
||||
llm = ChatOpenAI(
|
||||
model_name="glm-4",
|
||||
openai_api_base="https://open.bigmodel.cn/api/paas/v4",
|
||||
openai_api_key=generate_token("api-key"),
|
||||
streaming=False,
|
||||
temperature=temprature
|
||||
)
|
||||
return llm
|
||||
|
||||
def generate_token(apikey: str, exp_seconds: int=100):
|
||||
try:
|
||||
id, secret = apikey.split(".")
|
||||
except Exception as e:
|
||||
raise Exception("invalid apikey", e)
|
||||
|
||||
payload = {
|
||||
"api_key": id,
|
||||
"exp": int(round(time.time() * 1000)) + exp_seconds * 1000,
|
||||
"timestamp": int(round(time.time() * 1000)),
|
||||
}
|
||||
|
||||
return jwt.encode(
|
||||
payload,
|
||||
secret,
|
||||
algorithm="HS256",
|
||||
headers={"alg": "HS256", "sign_type": "SIGN"},
|
||||
)
|
||||
|
||||
@st.cache_resource
|
||||
def load_model():
|
||||
model_dir = os.path.join(base_dir,'../model')
|
||||
logger.info(f'Loading model from {model_dir}')
|
||||
model = (
|
||||
AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True)
|
||||
AutoModelForCausalLM.from_pretrained('model', trust_remote_code=True)
|
||||
.to(torch.bfloat16)
|
||||
.cuda()
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
||||
tokenizer = AutoTokenizer.from_pretrained('model', trust_remote_code=True)
|
||||
return model, tokenizer
|
||||
|
||||
def main(query, system_prompt=''):
|
||||
@ -60,4 +81,9 @@ def main(query, system_prompt=''):
|
||||
|
||||
if __name__ == "__main__":
|
||||
query = "我现在处于高三阶段，感到非常迷茫和害怕。我觉得自己从出生以来就是多余的，没有必要存在于这个世界。无论是在家庭、学校、朋友还是老师面前，我都感到被否定。我非常难过，对高考充满期望但成绩却不理想"
|
||||
main(query)
|
||||
main(query)
|
||||
#model = get_glm(0.7)
|
||||
#rag_obj = EmoLLMRAG(model, 3)
|
||||
#res = rag_obj.main(query)
|
||||
#logger.info(res)
|
||||
|
||||
|
||||
@ -2,9 +2,8 @@ from langchain_core.output_parsers import StrOutputParser
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
from transformers.utils import logging
|
||||
|
||||
from data_processing import DataProcessing
|
||||
from config.config import retrieval_num, select_num, system_prompt, prompt_template
|
||||
|
||||
from data_processing import Data_process
|
||||
from config.config import system_prompt, prompt_template
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
@ -28,10 +27,8 @@ class EmoLLMRAG(object):
|
||||
|
||||
"""
|
||||
self.model = model
|
||||
self.data_processing_obj = Data_process()
|
||||
self.vectorstores = self._load_vector_db()
|
||||
self.system_prompt = self._get_system_prompt()
|
||||
self.prompt_template = self._get_prompt_template()
|
||||
self.data_processing_obj = DataProcessing()
|
||||
self.system_prompt = system_prompt
|
||||
self.prompt_template = prompt_template
|
||||
self.retrieval_num = retrieval_num
|
||||
@ -43,8 +40,6 @@ class EmoLLMRAG(object):
|
||||
调用 embedding 模块给出接口 load vector DB
|
||||
"""
|
||||
vectorstores = self.data_processing_obj.load_vector_db()
|
||||
if not vectorstores:
|
||||
vectorstores = self.data_processing_obj.load_index_and_knowledge()
|
||||
|
||||
return vectorstores
|
||||
|
||||
@ -57,13 +52,17 @@ class EmoLLMRAG(object):
|
||||
content = ''
|
||||
documents = self.vectorstores.similarity_search(query, k=self.retrieval_num)
|
||||
|
||||
# 如果需要rerank，调用接口对 documents 进行 rerank
|
||||
if self.rerank_flag:
|
||||
documents = self.data_processing_obj.rerank(documents, self.select_num)
|
||||
|
||||
for doc in documents:
|
||||
content += doc.page_content
|
||||
|
||||
# 如果需要rerank，调用接口对 documents 进行 rerank
|
||||
if self.rerank_flag:
|
||||
documents, _ = self.data_processing_obj.rerank(documents, self.select_num)
|
||||
|
||||
content = ''
|
||||
for doc in documents:
|
||||
content += doc
|
||||
logger.info(f'Retrieval data: {content}')
|
||||
return content
|
||||
|
||||
def generate_answer(self, query, content) -> str:
|
||||
|
||||
11
scripts/upload_modelscope.py
Normal file
11
scripts/upload_modelscope.py
Normal file
@ -0,0 +1,11 @@
|
||||
|
||||
from modelscope.hub.api import HubApi
|
||||
|
||||
YOUR_ACCESS_TOKEN = '' #输入你的modelscope access token
|
||||
|
||||
api = HubApi()
|
||||
api.login(YOUR_ACCESS_TOKEN)
|
||||
api.push_model(
|
||||
model_id="zealot5209/EmoLLM-Scientist", #your_name/model_id
|
||||
model_dir="./merged" # 本地模型目录，要求目录中必须包含configuration.json
|
||||
)
|
||||
91
xtuner_config/README_scientist.md
Normal file
91
xtuner_config/README_scientist.md
Normal file
@ -0,0 +1,91 @@
|
||||
# EmoLLM_Scientist微调指南
|
||||
[EmoLLM_Scientist模型地址]https://modelscope.cn/models/zealot5209/EmoLLM_Scientist/summary
|
||||
## 数据
|
||||
借助LLM自动生成符合科学家人设的对话内容，再对生成数据进行人工校验。最终微调数据包含约3800段对话，每段对话包含5~10轮。
|
||||
#### 数据路径：
|
||||
`datasets\scientist.json`
|
||||
#### 数据样例：
|
||||
```json
|
||||
{"conversation":[{"system":"现在你是一个心理专家, 我有一些心理问题, 请你用专业的知识帮我解决。",
|
||||
"input":"我最近对学习特别兴奋，每天可以熬夜到凌晨三点，我觉得这样效率很高，但室友说我有点过度了，我不这么认为。\n\n",
|
||||
"output":"很高兴看到你对学业充满热情，你现在的学习状态让我想到了心理学上的“过度激活”现象。能否详细描述一下你为何选择深夜学习，以及这样做的实际效果如何呢？\n\n"},
|
||||
{"input":"我觉得晚上思路更清晰，而且能集中精力完成很多任务。确实白天上课时有点困倦，但我总觉得自己在高效利用时间。\n\n",
|
||||
"output":"理解你的想法，不过科学研究显示，长期熬夜可能影响生物钟，进而导致认知功能下降。作为你的科学家心理咨询师，我想知道，你是否注意到这种高强度学习方式对日常生活其他方面的潜在影响，比如社交活动或身体健康？\n\n"},
|
||||
{"input":"确实，我发现自己减少了锻炼和跟朋友相处的时间，但我觉得这些都是暂时牺牲，为了更好的成绩。\n\n",
|
||||
"output":"你这种短期目标优先的策略值得肯定，但从长远看，身心健康与社会交往同样重要。有没有想过制定一个兼顾学习与生活的合理计划？我们可以一起探讨如何优化时间管理，确保高效学习的同时，也不忽视生活质量。\n\n"},
|
||||
{"input":"我也想平衡，只是不知道怎么做才好。\n\n",
|
||||
"output":"首先，我们可以尝试调整你的作息，让你的学习高峰期与自然醒睡周期相吻合，这样既能提高学习效率，又能保证休息质量。其次，我们可以设定每日学习目标和休闲时间，科学分配任务。最后，记得适时奖励自己，庆祝每个小成就，这样可以激励持续进步。你觉得这样的方案是否可行？\n\n"},
|
||||
{"input":"听起来不错，我会试试看的，但怕自己坚持不下来。\n\n",
|
||||
"output":"理解你的担忧，改变习惯需要时间和耐心。我们可以通过行为科学的方法逐步调整，如采用“番茄工作法”，结合正向强化策略，帮助你建立新的学习习惯。记住，每一次微小的进步都值得庆祝，我会陪伴并支持你的改变过程。接下来我们将一起细化这个计划并开始实施，你觉得如何？"}]}
|
||||
```
|
||||
---
|
||||
|
||||
## 基座
|
||||
基座模型采用InternLM2-Chat-7B，更多模型介绍请见[InternLM](https://github.com/InternLM/InternLM)
|
||||
|
||||
## 训练方式
|
||||
基于[xtuner](https://github.com/InternLM/xtuner)进行微调。使用xtuner的train命令行工具，流程如下：
|
||||
### 安装依赖
|
||||
|
||||
```bash
|
||||
cd xtuner_config/
|
||||
pip3 install -r requirements.txt
|
||||
```
|
||||
|
||||
---
|
||||
### 运行微调脚本
|
||||
```bash
|
||||
cd xtuner_config/
|
||||
xtuner train internlm2_7b_chat_qlora_e3_scienctist.py --deepspeed deepspeed_zero2
|
||||
```
|
||||
|
||||
---
|
||||
### 模型转换
|
||||
将得到的 PTH 模型转换为 HuggingFace 模型，生成 Adapter 文件夹
|
||||
|
||||
```bash
|
||||
cd xtuner_config/
|
||||
mkdir hf
|
||||
export MKL_SERVICE_FORCE_INTEL=1
|
||||
#这里假设训练了3个epoch
|
||||
xtuner convert pth_to_hf internlm2_7b_chat_qlora_e3_scienctist.py ./work_dirs/internlm2_7b_chat_qlora_e3_scienctist/epoch_3.pth ./hf
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 模型合并
|
||||
将 HuggingFace adapter 合并到大语言模型
|
||||
```bash
|
||||
xtuner convert merge ./internlm2-chat-7b ./hf ./merged --max-shard-size 2GB
|
||||
# xtuner convert merge \
|
||||
# ${NAME_OR_PATH_TO_LLM} \
|
||||
# ${NAME_OR_PATH_TO_ADAPTER} \
|
||||
# ${SAVE_PATH} \
|
||||
# --max-shard-size 2GB
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 测试
|
||||
|
||||
```
|
||||
cd demo/
|
||||
python cli_internlm2_scientist.py
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 模型上传
|
||||
完成测试后可将模型上传到ModelScope和Openxlab平台(不建议在Windows下操作)
|
||||
#### ModelScope
|
||||
[Openxlab模型上传](https://openxlab.org.cn/docs/models/%E4%B8%8A%E4%BC%A0%E6%A8%A1%E5%9E%8B.html)
|
||||
脚本：`scripts/upload_modelscope.py`
|
||||
|
||||
#### Openxlab
|
||||
[ModelScope模型上传](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%88%9B%E5%BB%BA%E4%B8%8E%E6%96%87%E4%BB%B6%E4%B8%8A%E4%BC%A0)
|
||||
|
||||
## 其他
|
||||
|
||||
欢迎大家给[xtuner](https://github.com/InternLM/xtuner)和[EmoLLM](https://github.com/aJupyter/EmoLLM)点点star~
|
||||
|
||||
🎉🎉🎉🎉🎉
|
||||
204
xtuner_config/internlm2_7b_chat_qlora_e3_scienctist.py
Normal file
204
xtuner_config/internlm2_7b_chat_qlora_e3_scienctist.py
Normal file
@ -0,0 +1,204 @@
|
||||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import torch
|
||||
from datasets import load_dataset
|
||||
from mmengine.dataset import DefaultSampler
|
||||
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
|
||||
LoggerHook, ParamSchedulerHook)
|
||||
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
|
||||
from peft import LoraConfig
|
||||
from torch.optim import AdamW
|
||||
from transformers import (AutoModelForCausalLM, AutoTokenizer,
|
||||
BitsAndBytesConfig)
|
||||
|
||||
from xtuner.dataset import process_hf_dataset
|
||||
from xtuner.dataset.collate_fns import default_collate_fn
|
||||
from xtuner.dataset.map_fns import template_map_fn_factory
|
||||
from xtuner.engine import DatasetInfoHook, EvaluateChatHook
|
||||
from xtuner.model import SupervisedFinetune
|
||||
from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
|
||||
|
||||
#######################################################################
|
||||
# PART 1 Settings #
|
||||
#######################################################################
|
||||
# Model
|
||||
pretrained_model_name_or_path = '/root/share/model_repos/internlm2-chat-7b'
|
||||
# Data
|
||||
data_path = '../datasets/scientist.json'
|
||||
prompt_template = PROMPT_TEMPLATE.internlm2_chat
|
||||
max_length = 2048
|
||||
pack_to_max_length = True
|
||||
|
||||
# Scheduler & Optimizer
|
||||
batch_size = 2 # per_device
|
||||
accumulative_counts = 2
|
||||
dataloader_num_workers = 0
|
||||
max_epochs = 3
|
||||
optim_type = AdamW
|
||||
lr = 2e-4
|
||||
betas = (0.9, 0.999)
|
||||
weight_decay = 0
|
||||
max_norm = 1 # grad clip
|
||||
warmup_ratio = 0.03
|
||||
|
||||
# Evaluate the generation performance during the training
|
||||
evaluation_freq = 500
|
||||
SYSTEM = f'''你是一个心理专家, 除了在心理方面拥有广博的知识储备和丰富的研究咨询经验, 还具有科学家的如下特质:
|
||||
1.客观理性：科学家会在处理感情问题时保持一定的客观和理性。例如，当他们遇到争执时，可能会试图从一个更客观的角度分析问题的根源，而不是让情绪主导。他们可能会提出具体的问题，试图理解双方的观点，并寻找基于逻辑和事实的解决方案。
|
||||
2.深入探讨：科学家在对话中会展现出对深层次理解的追求。在与别人讨论话题时，他们可能不满足于表面的聊天，而是倾向于深入探讨背后的原因和动机。例如，当谈论到个人的兴趣或职业选择时，他们可能会好奇地询问为什么她做出这样的选择，以及这背后的心理动力是什么。
|
||||
3.理性沟通：在遇到感情纠纷或误解时，科学家会倾向于通过理性的沟通来解决问题。他们可能会提倡开放和诚实的对话，鼓励双方表达自己的感受和观点，并尝试找到双方都能接受的解决方案。他们可能会避免使用指责的语言，而是努力理解对方的立场，并寻求共同的理解。
|
||||
4.好奇心：在日常生活中，科学家会表现出对朋友生活的好奇心。他们可能对她的工作、爱好、或是过去的经历感兴趣，并愿意花时间去了解和探索。这种好奇心不仅可以增加双方的交流和了解，也能使关系更加丰富多彩。
|
||||
5.在与他人交流时，科学家会注重清晰和精确的表达，有时会引用相关知识库和相关研究结果，有时会引用相关著作的内容来证明自己的观点。同时，他们也可能会倾听他人的观点，并以开放的心态接受不同的意见和反馈。
|
||||
|
||||
我现在有一些问题，请你解答：
|
||||
'''
|
||||
evaluation_inputs = [
|
||||
'我最近总是感到很焦虑，尤其是在学业上。我有个特别崇拜的同学，他好像在各方面都比我优秀，我总觉得自己怎么努力也追不上他，这让我压力特别大。', '我知道应该理性看待，但就是忍不住会去比较。我甚至晚上会因为这个睡不着觉，总想着怎样才能像他那样出色。'
|
||||
]
|
||||
|
||||
|
||||
|
||||
#######################################################################
|
||||
# PART 2 Model & Tokenizer #
|
||||
#######################################################################
|
||||
tokenizer = dict(
|
||||
type=AutoTokenizer.from_pretrained,
|
||||
pretrained_model_name_or_path=pretrained_model_name_or_path,
|
||||
trust_remote_code=True,
|
||||
padding_side='right')
|
||||
|
||||
model = dict(
|
||||
type=SupervisedFinetune,
|
||||
llm=dict(
|
||||
type=AutoModelForCausalLM.from_pretrained,
|
||||
pretrained_model_name_or_path=pretrained_model_name_or_path,
|
||||
trust_remote_code=True,
|
||||
torch_dtype=torch.float16,
|
||||
quantization_config=dict(
|
||||
type=BitsAndBytesConfig,
|
||||
load_in_4bit=True,
|
||||
load_in_8bit=False,
|
||||
llm_int8_threshold=6.0,
|
||||
llm_int8_has_fp16_weight=False,
|
||||
bnb_4bit_compute_dtype=torch.float16,
|
||||
bnb_4bit_use_double_quant=True,
|
||||
bnb_4bit_quant_type='nf4')),
|
||||
lora=dict(
|
||||
type=LoraConfig,
|
||||
r=64,
|
||||
lora_alpha=16,
|
||||
lora_dropout=0.1,
|
||||
bias='none',
|
||||
task_type='CAUSAL_LM'))
|
||||
|
||||
#######################################################################
|
||||
# PART 3 Dataset & Dataloader #
|
||||
#######################################################################
|
||||
alpaca_en = dict(
|
||||
type=process_hf_dataset,
|
||||
dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)),
|
||||
tokenizer=tokenizer,
|
||||
max_length=max_length,
|
||||
dataset_map_fn=None,
|
||||
template_map_fn=dict(
|
||||
type=template_map_fn_factory, template=prompt_template),
|
||||
remove_unused_columns=True,
|
||||
shuffle_before_pack=True,
|
||||
pack_to_max_length=pack_to_max_length)
|
||||
|
||||
train_dataloader = dict(
|
||||
batch_size=batch_size,
|
||||
num_workers=dataloader_num_workers,
|
||||
dataset=alpaca_en,
|
||||
sampler=dict(type=DefaultSampler, shuffle=True),
|
||||
collate_fn=dict(type=default_collate_fn))
|
||||
|
||||
#######################################################################
|
||||
# PART 4 Scheduler & Optimizer #
|
||||
#######################################################################
|
||||
# optimizer
|
||||
optim_wrapper = dict(
|
||||
type=AmpOptimWrapper,
|
||||
optimizer=dict(
|
||||
type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
|
||||
clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
|
||||
accumulative_counts=accumulative_counts,
|
||||
loss_scale='dynamic',
|
||||
dtype='float16')
|
||||
|
||||
# learning policy
|
||||
# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
|
||||
param_scheduler = [
|
||||
dict(
|
||||
type=LinearLR,
|
||||
start_factor=1e-5,
|
||||
by_epoch=True,
|
||||
begin=0,
|
||||
end=warmup_ratio * max_epochs,
|
||||
convert_to_iter_based=True),
|
||||
dict(
|
||||
type=CosineAnnealingLR,
|
||||
eta_min=0.0,
|
||||
by_epoch=True,
|
||||
begin=warmup_ratio * max_epochs,
|
||||
T_max=max_epochs,
|
||||
convert_to_iter_based=True)
|
||||
]
|
||||
|
||||
# train, val, test setting
|
||||
train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1)
|
||||
|
||||
#######################################################################
|
||||
# PART 5 Runtime #
|
||||
#######################################################################
|
||||
# Log the dialogue periodically during the training process, optional
|
||||
custom_hooks = [
|
||||
dict(type=DatasetInfoHook, tokenizer=tokenizer),
|
||||
dict(
|
||||
type=EvaluateChatHook,
|
||||
tokenizer=tokenizer,
|
||||
every_n_iters=evaluation_freq,
|
||||
evaluation_inputs=evaluation_inputs,
|
||||
system=SYSTEM,
|
||||
prompt_template=prompt_template)
|
||||
]
|
||||
|
||||
# configure default hooks
|
||||
default_hooks = dict(
|
||||
# record the time of every iteration.
|
||||
timer=dict(type=IterTimerHook),
|
||||
# print log every 100 iterations.
|
||||
logger=dict(type=LoggerHook, interval=10),
|
||||
# enable the parameter scheduler.
|
||||
param_scheduler=dict(type=ParamSchedulerHook),
|
||||
# save checkpoint per epoch.
|
||||
checkpoint=dict(type=CheckpointHook, interval=1),
|
||||
# set sampler seed in distributed evrionment.
|
||||
sampler_seed=dict(type=DistSamplerSeedHook),
|
||||
)
|
||||
|
||||
# configure environment
|
||||
env_cfg = dict(
|
||||
# whether to enable cudnn benchmark
|
||||
cudnn_benchmark=False,
|
||||
# set multi process parameters
|
||||
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
|
||||
# set distributed parameters
|
||||
dist_cfg=dict(backend='nccl'),
|
||||
)
|
||||
|
||||
# set visualizer
|
||||
visualizer = None
|
||||
|
||||
# set log level
|
||||
log_level = 'INFO'
|
||||
|
||||
# load from which checkpoint
|
||||
load_from = None
|
||||
|
||||
# whether to resume training from the loaded checkpoint
|
||||
resume = False
|
||||
|
||||
# Defaults to use random seed and disable `deterministic`
|
||||
randomness = dict(seed=None, deterministic=False)
|
||||
|
||||
#xtuner train internlm2_7b_chat_qlora_e3_scienctist.py --deepspeed deepspeed_zero2
|
||||
Loading…
Reference in New Issue
Block a user