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.idea
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/Qwen3-0.6B
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git
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import os
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class Config:
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# 基座模型
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MODEL_NAME = "Qwen/Qwen3-0.6B"
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# 数据集
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DATASET_NAME = "krisfu/delicate_medical_r1_data"
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# 数据集主题（子数据集）
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DATASET_SUBJECT = "default"
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# 数据集用途
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DATASET_SPLIT = "train"
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# 是否使用缓存
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DATASET_USE_CACHE = True
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# swanlab项目名称
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SWANLAB_PROJECT = "qweb3-sft-medical-10-11-1"
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# 验证用system的提示词
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PROMPT = "你是一个医学专家，你需要根据用户的问题，给出带有思考的回答。"
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DATA_MAX_LENGTH = 2048
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class Default:
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DATASET_PATH = os.getenv("DATASET_PATH", "./dataset") # 支持环境变量覆
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MODEL_DATASET_PATH = os.getenv("MODEL_DATASET_PATH", "./model_dataset") # 支持环境变量覆
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SAVE_DIR = "./saved_model" # 微调后模型存储位置
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TRAIN_DATASET_FILE = "train.jsonl"
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TEST_DATASET_FILE = "val.jsonl"
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TRAIN_JSONL_NEW_FILE = "train_format.jsonl"
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TEST_JSONL_NEW_FILE = "val_format.jsonl"
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dataset_short_name = Config.DATASET_NAME.split("/")[-1]
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model_dataset_short_name = Config.MODEL_NAME.split("/")[-1]
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# 确保缓存目录存在
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dataset_dir = os.path.normpath(
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os.path.join(Default.DATASET_PATH, dataset_short_name, Config.DATASET_SUBJECT, Config.DATASET_SPLIT)
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)
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model_dataset_DIR = os.path.normpath(
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os.path.join(Default.MODEL_DATASET_PATH, model_dataset_short_name, Config.DATASET_SUBJECT, Config.DATASET_SPLIT)
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)
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model_dir = os.path.normpath(
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os.path.join(Default.SAVE_DIR,model_dataset_short_name, dataset_short_name, Config.DATASET_SUBJECT, Config.DATASET_SPLIT)
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)
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os.makedirs(dataset_dir, exist_ok=True)
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os.makedirs(model_dataset_DIR, exist_ok=True)
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os.makedirs(model_dir, exist_ok=True)
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class Dir:
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DATASET_DIR = dataset_dir
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MODEL_DIR = model_dir
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MODEL_DATASET_DIR = model_dataset_DIR
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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from config import Config,Default,Dir
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def get_model_train_dataset_json_file():
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model_train_json_file = Dir.MODEL_DATASET_DIR + "/" + Default.TRAIN_JSONL_NEW_FILE
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model_test_json_file = Dir.MODEL_DATASET_DIR + "/" + Default.TEST_JSONL_NEW_FILE
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return model_train_json_file, model_test_json_file
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def predict(messages, model, tokenizer):
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device = "cuda"
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=2048)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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# 加载原下载路径的tokenizer和model
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tokenizer = AutoTokenizer.from_pretrained("./Qwen3-0.6B/checkpoint-1084", use_fast=False, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("./Qwen3-0.6B/checkpoint-1084", device_map="auto", torch_dtype=torch.bfloat16)
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test_texts = {
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'instruction': "你是一个医学专家，你需要根据用户的问题，给出带有思考的回答。",
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'input': "医生，我在研究内耳的前庭部分时，发现了一些特殊的结构，比如前庭嵴。请问前庭内还有哪些特殊的结构，它们的作用是什么？"
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}
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instruction = test_texts['instruction']
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input_value = test_texts['input']
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messages = [
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{"role": "system", "content": f"{instruction}"},
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{"role": "user", "content": f"{input_value}"}
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]
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response = predict(messages, model, tokenizer)
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print(response)
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# 基础依赖
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modelscope
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yapf===0.32.0
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swanlab
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accelerate
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#pip install --upgrade modelscope -i https://mirrors.aliyun.com/pypi/simple/
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#pip install yapf==0.32.0 -i https://mirrors.aliyun.com/pypi/simple/
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#pip install swanlab -i https://mirrors.aliyun.com/pypi/simple/
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#pip install accelerate -i https://mirrors.aliyun.com/pypi/simple/
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import pandas as pd
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import torch
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from datasets import Dataset
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from modelscope import snapshot_download, AutoTokenizer
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from transformers import AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForSeq2Seq
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import os
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import swanlab
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from modelscope.msdatasets import MsDataset
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import json
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import random
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from config import Config,Default,Dir
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def dataset_jsonl_transfer(origin_path, new_path):
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"""
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将原始数据集转换为大模型微调所需数据格式的新数据集
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"""
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messages = []
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# 读取旧的JSONL文件
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with open(origin_path, "r") as file:
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for line in file:
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# 解析每一行的json数据
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data = json.loads(line)
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input = data["question"]
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output = f"<think>{data['think']}</think> \n {data['answer']}"
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message = {
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"instruction": Config.PROMPT,
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"input": f"{input}",
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"output": output,
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}
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messages.append(message)
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# 保存重构后的JSONL文件
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with open(new_path, "w", encoding="utf-8") as file:
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for message in messages:
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file.write(json.dumps(message, ensure_ascii=False) + "\n")
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def predict(messages, model, tokenizer):
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device = "cuda"
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=Config.DATA_MAX_LENGTH,
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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def load_model_and_tokenizer():
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print("🧼 开始加载模型...")
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# 在modelscope上下载Qwen模型到本地目录下
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model_dir = snapshot_download(Config.MODEL_NAME)
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# Transformers加载模型权重
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tokenizer = AutoTokenizer.from_pretrained(Config.MODEL_NAME, use_fast=False,
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trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto",
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dtype=torch.bfloat16)
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model.enable_input_require_grads() # 开启梯度检查点时，要执行该方法
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return model,tokenizer
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def mk_new_dataset_json(ds,dataset_train_json_file,dataset_test_json_file):
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data_list = list(ds)
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random.shuffle(data_list)
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split_idx = int(len(data_list) * 0.9)
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train_data = data_list[:split_idx]
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val_data = data_list[split_idx:]
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with open(dataset_train_json_file, 'w', encoding='utf-8') as f:
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for item in train_data:
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json.dump(item, f, ensure_ascii=False)
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f.write('\n')
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with open(dataset_test_json_file, 'w', encoding='utf-8') as f:
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for item in val_data:
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json.dump(item, f, ensure_ascii=False)
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f.write('\n')
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print(f"✅ 原始数据清洗完成 | 训练样本量: {len(train_data)} | 测试样本量: {len(train_data)}")
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def get_dataset_json():
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# 确保缓存目录存在
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os.makedirs(Dir.DATASET_DIR, exist_ok=True)
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ds = MsDataset.load(
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dataset_name=Config.DATASET_NAME,
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subset_name=Config.DATASET_SUBJECT,
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split=Config.DATASET_SPLIT,
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cache_dir=Dir.DATASET_DIR # 统一使用配置的路径
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)
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dataset_train_json_file = Dir.DATASET_DIR+"/"+Default.TRAIN_DATASET_FILE
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dataset_test_json_file = Dir.DATASET_DIR + "/" + Default.TEST_DATASET_FILE
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print(f"🏷️ 原始训练数据文件：{dataset_train_json_file}")
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print(f"🏷️ 原始测试数据文件：{dataset_test_json_file}")
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if (not os.path.exists(dataset_train_json_file)
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or not os.path.exists(dataset_test_json_file)
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or not Config.DATASET_USE_CACHE):
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print("🏷️ 正在生成原始数据集...")
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mk_new_dataset_json(ds,dataset_train_json_file,dataset_test_json_file)
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return dataset_train_json_file,dataset_test_json_file
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def get_model_train_dataset_json_file():
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model_train_json_file = Dir.MODEL_DATASET_DIR + "/" + Default.TRAIN_JSONL_NEW_FILE
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model_test_json_file = Dir.MODEL_DATASET_DIR + "/" + Default.TEST_JSONL_NEW_FILE
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return model_train_json_file, model_test_json_file
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def get_model_train_dataset_json():
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model_train_json_file,model_test_json_file = get_model_train_dataset_json_file()
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print(f"🏷️ 模型训练数据文件：{model_train_json_file}")
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print(f"🏷️ 模型测试数据文件：{model_test_json_file}")
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if not os.path.exists(model_train_json_file) or not os.path.exists(
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model_test_json_file) or not Config.DATASET_USE_CACHE:
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print("🏷️ 未找到模型对应数据集，准备从原始数据集进行生成数据集...")
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dataset_train_json_file,dataset_test_json_file = get_dataset_json()
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print("🏷️ 原始数据集生成成功...")
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print("🧼 开始转换模型训练数据集...")
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dataset_jsonl_transfer(dataset_train_json_file, model_train_json_file)
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print("🧼 开始转换模型验证数据集...")
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dataset_jsonl_transfer(dataset_test_json_file, model_test_json_file)
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return model_train_json_file,model_test_json_file
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def load_train_and_eval_data():
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#获取训练数据集json.
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print("🧼 开始获取训练数据...")
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model_train_json_file,model_test_json_file = get_model_train_dataset_json()
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# 得到训练集
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train_df = pd.read_json(model_train_json_file, lines=True)
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train_ds = Dataset.from_pandas(train_df)
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train_dataset = train_ds.map(process_func, remove_columns=train_ds.column_names)
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# 得到验证集
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eval_df = pd.read_json(model_test_json_file, lines=True)
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eval_ds = Dataset.from_pandas(eval_df)
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eval_dataset = eval_ds.map(process_func, remove_columns=eval_ds.column_names)
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return train_dataset,eval_dataset
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def set_swanlab():
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os.environ["SWANLAB_PROJECT"] = Config.SWANLAB_PROJECT
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swanlab.config.update({
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"model": "",
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"prompt": Config.PROMPT,
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"data_max_length": Config.DATA_MAX_LENGTH,
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})
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def process_func(example):
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"""
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将数据集进行预处理
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"""
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input_ids, attention_mask, labels = [], [], []
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instruction = tokenizer(
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f"<|im_start|>system\n{Config.PROMPT}<|im_end|>\n<|im_start|>user\n{example['input']}<|im_end|>\n<|im_start|>assistant\n",
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add_special_tokens=False,
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)
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response = tokenizer(f"{example['output']}", add_special_tokens=False)
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input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
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attention_mask = (
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instruction["attention_mask"] + response["attention_mask"] + [1]
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)
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labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
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if len(input_ids) > Config.DATA_MAX_LENGTH: # 做一个截断
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input_ids = input_ids[:Config.DATA_MAX_LENGTH]
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attention_mask = attention_mask[:Config.DATA_MAX_LENGTH]
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labels = labels[:Config.DATA_MAX_LENGTH]
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return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}
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def set_trainer(train_dataset):
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print("🧼 正在生成训练对象...")
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print(f"🧼 {Dir.MODEL_DIR}")
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args = TrainingArguments(
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output_dir=Dir.MODEL_DIR,
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per_device_train_batch_size=1,
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per_device_eval_batch_size=1,
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gradient_accumulation_steps=4,
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eval_strategy="steps",
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eval_steps=100,
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logging_steps=10,
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num_train_epochs=2,
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save_steps=400,
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learning_rate=1e-4,
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save_on_each_node=True,
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gradient_checkpointing=True,
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report_to="swanlab",
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run_name=Config.MODEL_NAME,
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)
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trainer = Trainer(
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model=model,
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args=args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
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)
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return trainer
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if __name__ == "__main__":
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#设置SWANLAB
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set_swanlab()
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#加载模型和分词器
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model, tokenizer = load_model_and_tokenizer()
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# 加载数据集和验证集
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train_dataset, eval_dataset = load_train_and_eval_data()
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model_train_json_file, model_test_json_file = get_model_train_dataset_json_file()
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#设置训练对象
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trainer = set_trainer(train_dataset)
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print("🚬🚬🚬 开始模型训练...")
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trainer.train()
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print(" 🎇🎇🎇模型训练完成，...")
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# 用测试集的前3条，主观看模型
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test_df = pd.read_json(model_train_json_file, lines=True)[:3]
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test_text_list = []
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for index, row in test_df.iterrows():
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instruction = row['instruction']
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input_value = row['input']
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messages = [
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{"role": "system", "content": f"{instruction}"},
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{"role": "user", "content": f"{input_value}"}
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]
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response = predict(messages, model, tokenizer)
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response_text = f"""
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Question: {input_value}
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LLM:{response}
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"""
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test_text_list.append(swanlab.Text(response_text))
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print(response_text)
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swanlab.log({"Prediction": test_text_list})
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swanlab.finish()
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from modelscope.msdatasets import MsDataset
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from modelscope.utils.hub import read_config
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from modelscope.trainers import build_trainer
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class Config:
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MODEL_NAME = "Qwen/Qwen3-0.6B"
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PRE_DEVICE_TRAIN_BATCH_SIZE = 1
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GRADIENT_ACCUMULATION_STEPS = 8
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LEARNING_RATE = 2e-5
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NUMBER_TRAIN_EPOCH = 500
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OUTPUT_DIR = "./qwen3_finetune"
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SAVE_STRATEGY = "epoch"
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FP16 = True
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LOGGING_DIR = "./logs"
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cfg = read_config(Config.MODEL_NAME)
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print(cfg)
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##自动下载
|
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#train_dataset = MsDataset.load('iic/chinese-kuakua-collection', subset_name='default', split='train')
|
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#test_dataset = MsDataset.load('iic/chinese-kuakua-collection', subset_name='default', split='test')
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#本地加载
|
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train_dataset = MsDataset.load('./chinese-kuakua-collection/train.csv')
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eval_dataset = MsDataset.load('./chinese-kuakua-collection/test.csv')
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||||
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def cfg_modify_fn(cfg):
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# cfg.preprocessor.type='sen-sim-tokenizer'
|
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# cfg.preprocessor.first_sequence = 'sentence1'
|
||||
# cfg.preprocessor.second_sequence = 'sentence2'
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# cfg.preprocessor.label = 'label'
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# cfg.preprocessor.label2id = {'0': 0, '1': 1}
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# cfg.model.num_labels = 2
|
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# cfg.task = 'text-classification'
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# cfg.pipeline = {'type': 'text-classification'}
|
||||
cfg.train.max_epochs = 5
|
||||
# cfg.train.work_dir = '/tmp'
|
||||
cfg.train.dataloader.batch_size_per_gpu = 32
|
||||
cfg.train.dataloader.workers_per_gpu = 0
|
||||
# cfg.evaluation.dataloader.batch_size_per_gpu = 32
|
||||
# cfg.train.dataloader.workers_per_gpu = 0
|
||||
# cfg.evaluation.dataloader.workers_per_gpu = 0
|
||||
# cfg.train.optimizer.lr = 2e-5
|
||||
# cfg.train.lr_scheduler.total_iters = int(len(train_dataset) / cfg.train.dataloader.batch_size_per_gpu) * cfg.train.max_epochs
|
||||
# cfg.evaluation.metrics = 'seq-cls-metric'
|
||||
# # 注意这里需要返回修改后的cfg
|
||||
return cfg
|
||||
|
||||
# 配置训练参数\
|
||||
kwargs = dict(
|
||||
model=Config.MODEL_NAME,
|
||||
train_dataset=train_dataset,
|
||||
eval_dataset=eval_dataset,
|
||||
cfg_modify_fn=cfg_modify_fn)
|
||||
trainer = build_trainer(default_args=kwargs)
|
||||
trainer.train()
|
||||
|
||||
Loading…
Reference in New Issue