GLM-4V-9B保姆级安装教程：Docker一键部署，支持多轮对话

GLM-4V-9B保姆级安装教程Docker一键部署支持多轮对话1. 环境准备与快速部署1.1 系统要求操作系统Linux (推荐Ubuntu 20.04)显卡NVIDIA GPU (显存≥24GB)驱动NVIDIA驱动≥515.65.01Docker19.03CUDA11.71.2 安装NVIDIA Container Toolkit# 添加NVIDIA官方GPG密钥 curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - # 添加仓库 distribution$(. /etc/os-release;echo $ID$VERSION_ID) curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list # 更新并安装 sudo apt-get update sudo apt-get install -y nvidia-container-toolkit sudo systemctl restart docker2. 一键部署GLM-4V-9B2.1 拉取Docker镜像docker pull registry.cn-hangzhou.aliyuncs.com/glm-4/glm-4v-9b:latest2.2 启动容器docker run -itd --gpus all -p 7860:7860 --name glm-4v \ -v /path/to/models:/models \ registry.cn-hangzhou.aliyuncs.com/glm-4/glm-4v-9b:latest3. 模型使用指南3.1 基础对话功能from transformers import AutoModelForCausalLM, AutoTokenizer model AutoModelForCausalLM.from_pretrained( THUDM/glm-4v-9b, trust_remote_codeTrue, device_mapauto ) tokenizer AutoTokenizer.from_pretrained(THUDM/glm-4v-9b, trust_remote_codeTrue) response model.chat(tokenizer, 描述这张图片, images[image.jpg]) print(response)3.2 多轮对话实现history [] while True: query input(用户输入: ) if query.lower() exit: break response model.chat( tokenizer, query, historyhistory, images[image.jpg] if 图片 in query else None ) print(AI:, response) history.append((query, response))4. 常见问题解决4.1 显存不足问题解决方案1使用INT4量化版本model AutoModelForCausalLM.from_pretrained( THUDM/glm-4v-9b-int4, trust_remote_codeTrue, device_mapauto )解决方案2启用梯度检查点model.gradient_checkpointing_enable()4.2 图片处理异常确保图片满足格式JPEG/PNG分辨率≤1120×1120通道RGB三通道5. 进阶配置5.1 自定义API服务from fastapi import FastAPI, UploadFile app FastAPI() app.post(/chat) async def chat_api(text: str, image: UploadFile None): image_data await image.read() if image else None response model.chat(tokenizer, text, images[image_data] if image_data else None) return {response: response}获取更多AI镜像想探索更多AI镜像和应用场景访问 CSDN星图镜像广场提供丰富的预置镜像覆盖大模型推理、图像生成、视频生成、模型微调等多个领域支持一键部署。