一、硬件準備

階段	推薦配置	最低要求
訓練階段	NVIDIA A100 80GB ×4	RTX 3090 24GB ×1
量化階段	Intel Xeon Gold 6248R CPU	i7-12700K + 64GB RAM
部署階段	Jetson Xavier NX開發套件	Raspberry Pi 4B 8GB

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二、軟件環境搭建

# 創建Python虛擬環境
conda create -n distil python=3.9
conda activate distil# 安裝核心依賴
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# 硬件加速庫
sudo apt install cuda-toolkit-11-7
conda install -c conda-forge cudatoolkit-dev=11.7

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三、分步驟實操流程

1. 教師模型準備

from transformers import AutoModelForSequenceClassification, AutoTokenizer# 加載DeepSeek模型
teacher = AutoModelForSequenceClassification.from_pretrained("deepseek-ai/deepseek-7b",num_labels=5  # 根據任務調整
)# 領域適配微調
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. 學生模型定義

import torch.nn as nnclass TinyLSTM(nn.Module):def __init__(self, vocab_size=30000, hidden_size=128):