1.环境准备

1.1 安装Rust环境安装Rust：确保已安装Rust编程语言。可以使用rustup工具进行安装和更新。

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# 安装 rustup
 curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
 source ~/.cargo/env

运行安装依赖库：使用Cargo管理依赖库。

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cargo new novel_finetune
cd novel_finetune
cargo add torch
cargo add rust-tensor

1.2 获取GPU资源

租用GPU卡：如果本地没有足够的GPU资源，可以租用云GPU卡（如DeepLearn账号）。注册并登录DeepLearn账号，领取赠额。选择4090或3090规格的GPU卡，确保显存至少24G。

2. 数据准备

2.1 小说数据预处理

数据格式化：将小说文本分割成适合模型训练的小段落。可以使用Python脚本进行处理。

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import re

def split_text_by_punctuation(text):
    sentences = re.split(r'。（|！|？|；|：|，|…', text)
    return sentences

with open('novel.txt', 'r', encoding='utf-8') as file:
    text = file.read()
    sentences = split_text_by_punctuation(text)
    with open('novel_sentences.json', 'w', encoding='utf-8') as json_file:
        json.dump(sentences, json_file, ensure_ascii=False, indent=4)

2.2 数据加载

加载数据：使用Rust读取JSON文件并加载数据。

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use std::fs::File;
use std::io::Read;
use serde_json;

fn load_data() -> Vec<String> {
  let mut file = File::open("novel_sentences.json").expect("Failed to open file");
  let mut contents = String::new();
  file.read_to_string(&mut contents).expect("Failed to read file");
  let Vec<String> = serde_json::from_str(&contents).expect("Failed to parse JSON");
  data
}

3. 模型选择与微调

3.1 模型选择

选择预训练模型：选择适合文本生成的预训练模型，如GPT-3或T5。模型微调：使用LoRA（Low-Rank Adaptation）或Flash Attention等技术进行微调。

3.2 微调步骤

设置训练参数：

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export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1
export CUDA_VISIBLE_DEVICES=0
export PyTorch_CUDA：%CUDA_VISIBLE_DEVICES%

训练代码：

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use torch::nn::{Module, Linear};
use torch::Tensor;
use torch::nn:: functional as F;
use torch::nn::module::Module;

struct FinetuneModel {
base_model: Box<dyn Module>,
adapter: Linear,
}

impl FinetuneModel {
fn new(base_model: Box<dyn Module>, adapter_dim: usize) -> Self {
FinetuneModel {
base_model,
adapter: Linear::new(base_model.output_dim(), adapter_dim),
}
}

fn forward(&self, x: &Tensor) -> Tensor {
let x = self.base_model.forward(x);
let x = self(adapter);
x
}
}

4. 训练与评估

4.1 训练过程

训练数据：将小说句子作为输入，生成下一句作为输出。训练代码：

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fn train(model: &mut FinetuneModel, optimizer: &mut Optimizer, epochs: usize) {
    for epoch in 0..epochs {
        for batch in &train_data {
let input = batch.input.to(device);
let target = batch.target.to(device);
let output = model.forward(&input);
let loss = F.cross_entropy(&output, &target);
optimizer.zero_grad();
loss.backward();
optimizer.step();
}
}
}

4.2 评估模型评估指标：使用准确率、困惑度等指标评估模型性能。

评估代码：

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fn evaluate(model: &FinetuneModel, &Vec<Batch>) -> f64 {
let mut total_loss = 0.0;
for batch in data {
let input = batch.input.to(device);
let target = batch.target.to(device);
let output = model.forward(&input);
let loss = F.cross_entropy(&output, &target);
total_loss += loss.item();
}
total_loss / data.len() as f64
}

5. 模型部署

5.1 模型导出

导出模型：将训练好的模型导出为ONNX或其他格式。

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torchscript --script model.py --extra-args "-o model.onnx"

5.2 部署代码

部署代码：使用Rust编写部署代码，加载模型并进行推理。

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use torch::nn::functional as F;
use torch::Tensor;

fn deploy(model_path: &str) -> FinetuneModel {
let model = FinetuneModel::load(model_path);
model.eval();
model
}

fn generate_text(model: &FinetuneModel, prompt: &str) -> String {
let input_tensor = Tensor::from_str(prompt).to(device);
let output = model.forward(&input_tensor);
let generated_text = output.argmax(dim=1).to_str();
generated_text.to_string()
}

6. 工程文件和目录结构

目录结构

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novel_finetune/
├── Cargo.toml
├── src/
│   ├── main.rs
│   ├── model.rs
│   ├── data.rs
│   └── train.rs
└── novel_sentences.json

文件内容 Cargo.toml：``` [package] name = "novel_finetune" version = "0.1.0" authors = ["Your Name your.email@example.com"] edition = "2018"

[dependencies] torch = "0.5" rust-tensor = "0.24" serde_json = "1.0" src/main.rs： mod model; mod data; mod train;

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fn main() {
let model = train::train();
let prompt = "这是小说的开头";
let generated_text = model::generate_text(&model, prompt);
println!("Generated text: {}", generated_text);
}

src/model.rs：

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use torch::nn::{Module, Linear};
use torch::Tensor;

struct FinetuneModel {
base_model: Box<dyn Module>,
adapter: Linear,
}

impl FinetuneModel {
fn new(base_model: Box<dyn Module>, adapter_dim: usize) -> Self {
FinetuneModel {
base_model,
adapter: Linear::new(base_model.output_dim(), adapter_dim),
}
}

fn forward(&self, x: &Tensor) -> Tensor {
let x = self.base_model.forward(x);
let x = self(adapter);
x
}
}

src/data.rs：

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use std::fs::File;
use std::io::Read;
use serde_json;

fn load_data() -> Vec<String> {
// Load data from JSON file
}
src/train.rs：
use torch::nn::{Module, Linear};
use torch::Tensor;
use torch::nn::functional as F;
use torch::nn::module::Module;

struct FinetuneModel {
base_model: Box<dyn Module>,
adapter: Linear,
}

impl FinetuneModel {
fn new(base_model: Box<dyn Module>, adapter_dim: usize) -> Self {
FinetuneModel {
base_model,
adapter: Linear::new(base_model.output_dim(), adapter_dim),
}
}

fn forward(&self, x: &Tensor) -> Tensor {
let x = self.base_model.forward(x);
let x = self(adapter);
x
}
}

fn train(model: &mut FinetuneModel, optimizer: &mut Optimizer, epochs: usize) {
// Training code
}

fn evaluate(model: &FinetuneModel, &Vec<Batch>) -> f64 {
// Evaluation code
}

总结

通过上述步骤，可以在个人PC上使用Rust对一本三百多万字的网络小说进行大模型微调。整个过程包括环境准备、数据预处理、模型选择与微调、训练与评估以及模型部署

1.环境准备

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