完整教程：LORA微调LLaMA3并结合RAG和Agent技术实现Text2SQL任务

环境准备

首先，安装必要的Python包：

pip install transformers peft datasets torch faiss-cpu

加载LLaMA3模型

从Hugging Face加载LLaMA3模型和对应的tokenizer：

from transformers import AutoTokenizer, AutoModelForCausalLMtokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")

准备数据集

加载Spider数据集：

from datasets import load_datasetdataset = load_dataset("spider")
train_data = dataset['train']
valid_data = dataset['validation']

LORA微调配置

配置LORA参数并应用到模型上：

from peft import LoraConfig, get_peft_modellora_config = LoraConfig(r=16,lora_alpha=32,lora_dropout=0.1,target_modules=["q_proj", "v_proj"]
)model = get_peft_model(model, lora_config)

数据预处理

定义数据预处理函数并处理训练和验证数据：

def preprocess_function(examples):inputs = [f"translate English to SQL: {query}" for query in examples["question"]]targets = [sql for sql in examples["query"]]model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding="max_length", return_tensors="pt")labels = tokenizer(targets, max_length=512, truncation=True, padding="max_length", return_tensors="pt")model_inputs["labels"] = labels["input_ids"]return model_inputstrain_dataset = train_data.map(preprocess_function, batched=True)
valid_dataset = valid_data.map(preprocess_function, batched=True)

自定义训练循环

实现自定义训练循环：

import torch
from torch.utils.data import DataLoadertrain_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)
valid_dataloader = DataLoader(valid_dataset, batch_size=8)optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)for epoch in range(3):  # 假设训练3个epochmodel.train()for batch in train_dataloader:optimizer.zero_grad()outputs = model(input_ids=batch['input_ids'], labels=batch['labels'])loss = outputs.lossloss.backward()optimizer.step()model.eval()eval_loss = 0with torch.no_grad():for batch in valid_dataloader:outputs = model(input_ids=batch['input_ids'], labels=batch['labels'])eval_loss += outputs.loss.item()print(f"Epoch {epoch+1}, Validation Loss: {eval_loss / len(valid_dataloader)}")

结合RAG技术

设置FAISS检索器，并结合检索与生成：

import faiss
import numpy as np
from transformers import AutoTokenizer# 假设我们有一个语料库
corpus = ["Example sentence 1.", "Example sentence 2.", "Example sentence 3."]# 将语料库句子转换为token IDs
corpus_inputs = tokenizer(corpus, return_tensors='pt', padding=True, truncation=True)# 使用模型生成语料库句子的embedding
with torch.no_grad():corpus_outputs = model(**corpus_inputs)# 获取最后一层隐藏状态的平均值作为句子的embedding
corpus_embeddings = torch.mean(corpus_outputs.last_hidden_state, dim=1).numpy()# 构建FAISS索引
index = faiss.IndexFlatL2(corpus_embeddings.shape[1])
index.add(corpus_embeddings)# 结合RAG技术与检索
def retrieve_and_generate(query, context_size=3, max_length=128, num_return_sequences=1):# 使用FAISS检索最相关的文档query_inputs = tokenizer(query, return_tensors='pt', padding=True, truncation=True)with torch.no_grad():query_embedding = model(**query_inputs).last_hidden_state.mean(dim=1).numpy()D, I = index.search(query_embedding, k=context_size)retrieved_docs = [corpus[i] for i in I[0]]context = " ".join(retrieved_docs)# 将检索到的文档与查询结合input_with_context = f"{context} {query}"# 生成查询的SQLinputs = tokenizer(input_with_context, return_tensors="pt", max_length=max_length, truncation=True)with torch.no_grad():outputs = model.generate(**inputs, num_return_sequences=num_return_sequences)# 返回生成的SQL查询return tokenizer.decode(outputs[0], skip_special_tokens=True)# 示例
query = "Show all users"
sql_query = retrieve_and_generate(query)
print(sql_query)

结合Agent技术

实现NLU组件和对话管理：

from transformers import pipelinenlu = pipeline("ner")def parse_input(user_input):entities = nlu(user_input)if "users" in user_input.lower():return "SELECT * FROM users"else:return "Query not recognized"class Agent:def __init__(self):self.context = ""def handle_input(self, user_input):self.context += f" {user_input}"sql_query = parse_input(self.context)return sql_queryagent = Agent()
user_input = "Show all users"
response = agent.handle_input(user_input)
print(response)  # 输出: SELECT * FROM users

模型保存与部署

保存微调后的模型：

model.save_pretrained("./finetuned_llama3")
tokenizer.save_pretrained("./finetuned_llama3")

总结

通过以上步骤，我们从头到尾实现了使用LORA微调LLaMA3模型，并结合RAG和Agent技术进行Text2SQL任务。这个流程包括环境准备、数据预处理、自定义训练循环、RAG技术整合、Agent实现，以及最终的模型保存。