DeepSeek蒸馏TinyLSTM实操指南

一、硬件准备

阶段	推荐配置	最低要求
训练阶段	NVIDIA A100 80GB ×4	RTX 3090 24GB ×1
量化阶段	Intel Xeon Gold 6248R CPU	i7-12700K + 64GB RAM
部署阶段	Jetson Xavier NX开发套件	Raspberry Pi 4B 8GB

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二、软件环境搭建

# 创建Python虚拟环境
conda create -n distil python=3.9
conda activate distil

# 安装核心依赖
pip install torch==2.0.1+cu117 -f https://download.pytorch.org/whl/torch_stable.html
pip install transformers==4.31.0 datasets==2.13.1
pip install onnx==1.14.0 onnxruntime==1.15.1
pip install tensorrt==8.6.1 --extra-index-url https://pypi.ngc.nvidia.com

# 硬件加速库
sudo apt install cuda-toolkit-11-7
conda install -c conda-forge cudatoolkit-dev=11.7

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三、分步骤实操流程

1. 教师模型准备

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# 加载DeepSeek模型
teacher = AutoModelForSequenceClassification.from_pretrained(
    "deepseek-ai/deepseek-7b",
    num_labels=5  # 根据任务调整
)

# 领域适配微调
from datasets import load_dataset
ds = load_dataset("your_dataset")

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=8,
    fp16=True
)

trainer = Trainer(
    model=teacher,
    args=training_args,
    train_dataset=ds["train"]
)
trainer.train()

2. 学生模型定义

import torch.nn as nn

class TinyLSTM(nn.Module):
    def __init__(self, vocab_size=30000, hidden_size=128):