feat: add llama fine tuning

.gitignore

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+result/

llama-fine-tuning-QLoRA.py

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+import os
+
+# 切换到当前文件所在的目录
+os.chdir(os.path.dirname(__file__))
+
+# 导入必要的库
+import torch
+from datasets import load_dataset, Dataset
+from transformers import (
+    AutoModelForCausalLM,  # 用于加载预训练的语言模型
+    AutoTokenizer,  # 用于加载与模型相匹配的分词器
+    BitsAndBytesConfig,  # 用于配置4-bit量化
+    HfArgumentParser,  # 用于解析命令行参数
+    TrainingArguments,  # 用于设置训练参数
+    pipeline,  # 用于创建模型的pipeline
+    logging,  # 用于记录日志
+)
+from peft import LoraConfig, PeftModel  # 用于配置和加载QLoRA模型
+from trl import SFTTrainer  # 用于执行监督式微调的Trainer
+
+# 设置预训练模型的名称
+model_name = "meta-llama/Llama-3.1-8B-Instruct"
+
+# 设置微调后模型的名称
+new_model = "Llama-3.1-8b-Instruct-fine-tuned"
+
+# LoRA的注意力维度
+lora_r = 64
+
+# Alpha参数用于LoRA缩放
+lora_alpha = 16
+
+# LoRA层的dropout概率
+lora_dropout = 0.1
+
+# 激活4-bit精度的基础模型加载
+use_4bit = True
+
+# 4-bit基础模型的计算数据类型
+bnb_4bit_compute_dtype = "float16"
+
+# 4-bit量化类型（fp4或nf4）
+bnb_4bit_quant_type = "nf4"
+
+# 激活4-bit基础模型的嵌套量化（双重量化）
+use_nested_quant = False
+
+# 输出目录，用于存储模型预测和检查点
+output_dir = "./results"
+
+# 训练周期数
+num_train_epochs = 1
+
+# 是否启用fp16/bf16训练（在A100上将bf16设置为True）
+fp16 = False
+bf16 = True
+
+# GPU上每个训练批次的样本数
+per_device_train_batch_size = 4
+
+# GPU上每个评估批次的样本数
+per_device_eval_batch_size = 4
+
+# 累积梯度的更新步骤数
+gradient_accumulation_steps = 1
+
+# 是否启用梯度检查点
+gradient_checkpointing = True
+
+# 最大梯度归一化（梯度裁剪）
+max_grad_norm = 0.3
+
+# 初始学习率（AdamW优化器）
+learning_rate = 2e-4
+
+# 权重衰减，应用于全部layer（不包括bias/LayerNorm的权重）
+weight_decay = 0.001
+
+# 优化器
+optim = "paged_adamw_32bit"
+
+# 学习率计划
+lr_scheduler_type = "cosine"
+
+# 训练步数（覆盖num_train_epochs）
+max_steps = -1
+
+# 线性预热的步数比率（从0到学习率）
+warmup_ratio = 0.03
+
+# 按长度分组序列
+group_by_length = True
+
+# 每X更新步骤保存检查点
+save_steps = 0
+
+# 每X更新步骤记录日志
+logging_steps = 25
+
+# SFT参数配置
+# 最大序列长度
+max_seq_length = None
+
+# 打包多个短示例到同一输入序列以提高效率
+packing = False
+
+# 将整个模型加载到 GPU 0
+device_map = {"": 0}
+
+# 加载数据集
+dataset = load_dataset(path="json", data_dir="./num_list", data_files="num_list_500_per_sample_100_length.json")
+fine_tune_dataset = []
+print("Loading dataset...")
+for instance in dataset["train"]:
+    prompt = instance["system_prompt"] + "\n\n" + instance["description"] + "\nQuestion: " + instance["data"]["question"] + "\nData: " + instance["data"]["struct_data"]
+    answer = instance["data"]["answer"]
+    completion = f"The answer is {answer}."
+    fine_tune_dataset.append({"prompt": prompt, "completion": completion})
+
+fine_tune_dataset = Dataset.from_list(fine_tune_dataset)
+
+compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=use_4bit,
+    bnb_4bit_quant_type=bnb_4bit_quant_type,
+    bnb_4bit_compute_dtype=compute_dtype,
+    bnb_4bit_use_double_quant=use_nested_quant,
+)
+
+if compute_dtype == torch.float16 and use_4bit:
+    major, _ = torch.cuda.get_device_capability()
+    if major >= 8:
+        print("GPU支持bfloat16")
+
+# 加载模型
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map=device_map
+)
+model.config.use_cache = False
+model.config.pretraining_tp = 1
+
+# 加载分词器
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"  # 修复fp16训练中的溢出问题
+
+peft_config = LoraConfig(
+    lora_alpha=lora_alpha,
+    lora_dropout=lora_dropout,
+    r=lora_r,
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+training_arguments = TrainingArguments(
+    output_dir=output_dir,
+    num_train_epochs=num_train_epochs,
+    per_device_train_batch_size=per_device_train_batch_size,
+    gradient_accumulation_steps=gradient_accumulation_steps,
+    optim=optim,
+    save_steps=save_steps,
+    logging_steps=logging_steps,
+    learning_rate=learning_rate,
+    weight_decay=weight_decay,
+    fp16=fp16,
+    bf16=bf16,
+    max_grad_norm=max_grad_norm,
+    max_steps=max_steps,
+    warmup_ratio=warmup_ratio,
+    group_by_length=group_by_length,
+    lr_scheduler_type=lr_scheduler_type,
+    report_to="tensorboard",
+)
+
+# 设置监督式微调参数
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=fine_tune_dataset,
+    peft_config=peft_config,
+    dataset_text_field="text",
+    max_seq_length=max_seq_length,
+    tokenizer=tokenizer,
+    args=training_arguments,
+    packing=packing,
+)
+
+# 训练模型
+trainer.train()
+
+trainer.model.save_pretrained(new_model)