Qwen2-1.5B-Instruct Lora微调
十分钟ll 2024-08-19 09:01:05 阅读 89
Qwen2-1.5B-Instruct微调Lora微调
1. 模型下载2. 准备工作（高手请忽略，没啥用）3. 接下来进入正题吧（导包）4. 加载数据5. 数据预处理6. 创建模型7. 配置训练参数8. 创建训练器9. 开始训练！！！10. 完整的.py代码11. 合并Lora推理预测代码
最近做了一个基于Qwen2-1.5B-Instruct模型的比赛，记录一下自己的微调过程。怕自己以后忘了我就手把手一步一步来记录了。
大多数都是给小白看的，如果你是小白建议你用jupyter运行，按照我这个模块一块一块运行，如果你是高手单纯的想找一个训练代码直接看模块10，我在提供了完整代码。
1. 模型下载
一般模型尽量在modelscope上先搜一下，比较这个下载速度真的快。
<code>import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
import os
# 第一个参数表示下载模型的型号，第二个参数是下载后存放的缓存地址，第三个表示版本号，默认 master
model_dir = snapshot_download('Qwen/Qwen2-1.5B-Instruct', cache_dir='./', revision='master')code>
2. 准备工作（高手请忽略，没啥用）
微调的主要工作其实就是数据处理，其他基本就是个架往里放就行。
接下来是一份官网给出的推理的代码，借助这个代码我们来看输入模型的数据格式长什么样。
from modelscope import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"./Qwen2-1.5B-Instruct",
torch_dtype="auto",code>
device_map="auto"code>
)
tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen2-1.5B-Instruct")
prompt = "你好"
messages = [{ "role": "system", "content": '你是医疗问答助手章鱼哥，你将帮助用户解答基础的医疗问题。'},
{ "role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)code>
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
可以打印看看编码后的输入数据长什么样：
'<|im_start|>system\n你是医疗问答助手章鱼哥，你将帮助用户解答基础的医疗问题。<|im_end|>\n<|im_start|>user\n你好<|im_end|>\n<|im_start|>assistant\n'
这里可以看到其实apply_chat_template方法在对函数编码的时候没有给出mask等内容（他这个和智谱轻言的GLM的apply_chat_template就差距很大,在这卡了我半天）所以在数据处理的时候就不能直接用他这个模板。
3. 接下来进入正题吧（导包）
from datasets import Dataset, load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer
4. 加载数据
我这里是用了一个医疗问答的csv数据，能了解到这里的应该数据处理不需要细说了吧
dataset = load_dataset("csv", data_files="./问答.csv", split="train")code>
dataset = dataset.filter(lambda x: x["answer"] is not None)
datasets = dataset.train_test_split(test_size=0.1)
5. 数据预处理
tokenizer = AutoTokenizer.from_pretrained("./Qwen2-1.5B-Instruct", trust_remote_code=True)
def process_func(example):
MAX_LENGTH = 768 # 最大输入长度，根据你的显存和数据自己调整
input_ids, attention_mask, labels = [], [], []
instruction = example["question"].strip() # query
# instruction = tokenizer.apply_chat_template([{"role": "user", "content": instruction}],
# add_generation_prompt=True,
# tokenize=True,
# ) # '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nquery<|im_end|>\n<|im_start|>assistant\n'
instruction = tokenizer(
f"<|im_start|>system\n你是医学领域的人工助手章鱼哥<|im_end|>\n<|im_start|>user\n{ example['question']}<|im_end|>\n<|im_start|>assistant\n",
add_special_tokens=False,
)
response = tokenizer(f"{ example['answer']}", add_special_tokens=False) # \n response, 缺少eos token
input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
attention_mask = (instruction["attention_mask"] + response["attention_mask"] + [1])
labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
if len(input_ids) > MAX_LENGTH:
input_ids = input_ids[:MAX_LENGTH]
attention_mask = attention_mask[:MAX_LENGTH]
labels = labels[:MAX_LENGTH]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": labels
}
tokenized_ds = datasets['train'].map(process_func, remove_columns=['id', 'question', 'answer'])
tokenized_ts = datasets['test'].map(process_func, remove_columns=['id', 'question', 'answer'])
6. 创建模型
import torch
model = AutoModelForCausalLM.from_pretrained("./Qwen2-1.5B-Instruct", trust_remote_code=True)
from peft import LoraConfig, TaskType, get_peft_model, PeftModel
config = LoraConfig(target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"], modules_to_save=["post_attention_layernorm"]) # 配置Lora参数
model = get_peft_model(model, config) # 创建Lora模型
7. 配置训练参数
args = TrainingArguments(
output_dir="./chatbot",code>
per_device_train_batch_size=4,
gradient_accumulation_steps=8,
gradient_checkpointing=True,
logging_steps=300,
num_train_epochs=10,
learning_rate=1e-4,
remove_unused_columns=False,
save_strategy="epoch"code>
) # 在这里如果你开起了梯度检查点gradient_checkpointing=True，就必须加上model.enable_input_require_grads()，否则会报一个很难受的错误
model.enable_input_require_grads()
8. 创建训练器
trainer = Trainer(
model=model,
args=args,
train_dataset=tokenized_ds.select(range(5000)), # 我这个数据量很大，我随机抽取5000条训练不然太慢了
data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
)
9. 开始训练！！！
祝你成功
trainer.train()
10. 完整的.py代码
import torch
from datasets import Dataset, load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer
from peft import LoraConfig, TaskType, get_peft_model, PeftModel
dataset = load_dataset("csv", data_files="./问答.csv", split="train")code>
dataset = dataset.filter(lambda x: x["answer"] is not None)
datasets = dataset.train_test_split(test_size=0.1)
tokenizer = AutoTokenizer.from_pretrained("./Qwen2-1.5B-Instruct", trust_remote_code=True)
def process_func(example):
MAX_LENGTH = 768
input_ids, attention_mask, labels = [], [], []
instruction = example["question"].strip() # query
instruction = tokenizer(
f"<|im_start|>system\n你是医学领域的人工助手章鱼哥<|im_end|>\n<|im_start|>user\n{ example['question']}<|im_end|>\n<|im_start|>assistant\n",
add_special_tokens=False,
)
response = tokenizer(f"{ example['answer']}", add_special_tokens=False) # \n response, 缺少eos token
input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
attention_mask = (instruction["attention_mask"] + response["attention_mask"] + [1])
labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
if len(input_ids) > MAX_LENGTH:
input_ids = input_ids[:MAX_LENGTH]
attention_mask = attention_mask[:MAX_LENGTH]
labels = labels[:MAX_LENGTH]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": labels
}
tokenized_ds = datasets['train'].map(process_func, remove_columns=['id', 'question', 'answer'])
tokenized_ts = datasets['test'].map(process_func, remove_columns=['id', 'question', 'answer'])
model = AutoModelForCausalLM.from_pretrained("./Qwen2-1.5B-Instruct", trust_remote_code=True)
config = LoraConfig(target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"], modules_to_save=["post_attention_layernorm"])
model = get_peft_model(model, config)
args = TrainingArguments(
output_dir="./law",code>
per_device_train_batch_size=4,
gradient_accumulation_steps=16,
gradient_checkpointing=True,
logging_steps=6,
num_train_epochs=10,
learning_rate=1e-4,
remove_unused_columns=False,
save_strategy="epoch"code>
)
model.enable_input_require_grads()
trainer = Trainer(
model=model,
args=args,
train_dataset=tokenized_ds.select(range(400)),
data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
)
trainer.train()
11. 合并Lora推理预测代码
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
def predict(messages, model, tokenizer):
device = "cuda"
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(device)code>
generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response
# 加载原下载路径的tokenizer和model
tokenizer = AutoTokenizer.from_pretrained("./Qwen2-1.5B-Instruct/", use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("./Qwen2-1.5B-Instruct/", device_map="auto", torch_dtype=torch.bfloat16)code>
# 加载训练好的Lora模型，将下面的checkpointXXX替换为实际的checkpoint文件名名称
model = PeftModel.from_pretrained(model, model_id="./chatbot/checkpoint-1560")code>
test_texts = {
'instruction': "你是医学领域的人工助手章鱼哥",
'input': "嗓子疼，是不是得了流感了"
}
instruction = test_texts['instruction']
input_value = test_texts['input']
messages = [
{ "role": "system", "content": f"{ instruction}"},
{ "role": "user", "content": f"{ input_value}"}
]
response = predict(messages, model, tokenizer)
print(response)
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