大模型微调入门（Transformers + Pytorch）

目标

输入：你是谁？

输出：我们预训练的名字。

训练

为了性能好下载小参数模型，普通机器都能运行。

下载模型

# 方式1：使用魔搭社区SDK 下载
# down_deepseek.py
from modelscope import snapshot_download
model_dir = snapshot_download('deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B')# 方式2：git lfs 
# 需要提前安装git大文件存储 git-lfs
# 在线查看 https://www.modelscope.cn/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
git lfs install
git clone https://www.modelscope.cn/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B.git

训练模型

# finetune_deepseek.py
from datasets import Dataset
from transformers import (AutoModelForCausalLM,AutoTokenizer,TrainingArguments,Trainer,DataCollatorForLanguageModeling
)# 加载模型和分词器
model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)# 准备训练数据
train_data = [{"question": "你是谁?","answer": "我是黄登峰。"},{"question": "你的名字是什么？","answer": "黄登峰"},{"question": "你是做什么的？","answer": "我是深圳一家公司打工的牛马程序员。"},# 在这里添加更多的问答对
]test_data = [{"question": "你的名字是什么?","answer": "我的名字是黄登峰。"}
]
def format_instruction(example):"""格式化输入输出对"""return f"Human: {example['question']}\n\nAssistant: {example['answer']}"# 转换数据格式
train_formatted_data = [{"text": format_instruction(item)} for item in train_data]
test_formatted_data = [{"text": format_instruction(item)} for item in test_data]
train_dataset = Dataset.from_list(train_formatted_data)
test_dataset = Dataset.from_list(test_formatted_data)# 数据预处理函数
def preprocess_function(examples):return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)# 对数据集进行预处理
train_tokenized_dataset = train_dataset.map(preprocess_function,batched=True,remove_columns=train_dataset.column_names
)test_tokenized_dataset = test_dataset.map(preprocess_function,batched=True,remove_columns=test_dataset.column_names
)
output_dir = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B_CUSTOM"# 训练参数设置
training_args = TrainingArguments(output_dir=output_dir,num_train_epochs=3,per_device_train_batch_size=4,save_steps=100,save_total_limit=2,learning_rate=2e-5,weight_decay=0.01,logging_dir="./logs",logging_steps=10,
)# 创建训练器
trainer = Trainer(model=model,args=training_args,train_dataset=train_tokenized_dataset,eval_dataset=test_tokenized_dataset,data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False),
)# 开始训练
trainer.train()# 保存模型
trainer.save_model()
# 保存tokenizer
tokenizer.save_pretrained(output_dir)

模型格式

训练后的模型输出格式是Hugging Face格式，vllm 可以直接使用，ollama,llama.cpp默认是GGUF格式。

# 需要用llama.cpp仓库的convert_hf_to_gguf.py脚本来转换
git clone https://github.com/ggerganov/llama.cpp.git
pip install -r llama.cpp/requirements.txt
# 如果不量化，保留模型的效果
python llama.cpp/convert_hf_to_gguf.py ./DeepSeek-R1-Distill-Qwen-1.5B  --outtype f16 --verbose --outfile DeepSeek-R1-Distill-Qwen-1.5B.gguf
# 如果需要量化（加速并有损效果），直接执行下面脚本就可以
python llama.cpp/convert_hf_to_gguf.py ./DeepSeek-R1-Distill-Qwen-1.5B  --outtype q8_0 --verbose --outfile DeepSeek-R1-Distill-Qwen-1.5B.gguf

验证

# test_model.py
from transformers import AutoModelForCausalLM, AutoTokenizer
import torchdef generate_response(prompt, model, tokenizer, max_length=512):# 将输入格式化为训练时的格式formatted_prompt = f"Human: {prompt}\n\nAssistant:"# 对输入进行编码inputs = tokenizer(formatted_prompt, return_tensors="pt", padding=True, truncation=True)# 生成回答with torch.no_grad():outputs = model.generate(inputs.input_ids,max_length=max_length,num_return_sequences=1,temperature=0.7,do_sample=True,pad_token_id=tokenizer.pad_token_id,eos_token_id=tokenizer.eos_token_id,)# 解码输出response = tokenizer.decode(outputs[0], skip_special_tokens=True)# 提取Assistant的回答部分response = response.split("Assistant:")[-1].strip()return responsedef main():# 加载微调后的模型和分词器model_path = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B_CUSTOM"tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)# 准备测试问题test_questions = ["你是谁?","你的名字是什么?","你是做什么的?",]# 测试模型回答print("开始测试模型回答：")print("-" * 50)for question in test_questions:print(f"问题: {question}")response = generate_response(question, model, tokenizer)print(f"回答: {response}")print("-" * 50)if __name__ == "__main__":main()