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 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 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 pypi.ngc.nvidia # 硬件加速库 sudo apt install cuda-toolkit-11-7 conda install -c conda-forge cudatoolkit-dev=11.7 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):