Auto-sync: 2026-04-17 08:37

This commit is contained in:
2026-04-17 08:38:12 +08:00
parent 6caa1c2f47
commit a0a48bd334
247 changed files with 6577 additions and 3061 deletions

View File

@@ -1,5 +1,5 @@
---
title: Nano-Banana Pro: Prompting Guide & Strategies
title: Nano-Banana Pro:Prompting Guide & Strategies
source: https://dev.to/googleai/nano-banana-pro-prompting-guide-strategies-1h9n
author: shenwei
published: 2025-11-28

View File

@@ -1,5 +1,5 @@
---
title: codecrafters-io/build-your-own-x: Master programming by recreating your favorite technologies from scratch.
title: codecrafters-io/build-your-own-x:Master programming by recreating your favorite technologies from scratch.
source: https://github.com/codecrafters-io/build-your-own-x?tab=readme-ov-file#build-your-own-insert-technology-here
author: shenwei
published:

View File

@@ -0,0 +1,429 @@
---
title: 一、系统要求
source:
author: shenwei
published:
created:
description:
tags: [ollama, openclaw, qwen, qwen-coder, ubuntu]
---
#ubuntu #ollama #qwen-coder #qwen #openclaw
```table-of-contents
```
# 一、系统要求
运行 `qwen2.5-coder:7b` 推荐配置:
| 资源 | 最低 | 推荐 |
| ---- | ------- | ---------- |
| CPU | 4 cores | 8+ cores |
| RAM | 8GB | 16GB |
| GPU | 无需 | NVIDIA GPU |
| Disk | 10GB | 20GB |
| | | |
模型大小:
```
约 4.5GB
```
---
# 二、Ubuntu 安装 Ollama
## 1 更新系统
```bash
sudo apt update
sudo apt upgrade -y
```
安装 curl
```bash
sudo apt install -y curl
```
---
## 2 安装 Ollama
执行官方安装脚本:
```bash
curl -fsSL https://ollama.com/install.sh | sh
```
安装过程会自动:
- 安装 `ollama` CLI
- 创建 systemd 服务
- 启动 Ollama API
---
## 3 验证安装
```bash
ollama --version
```
示例:
```
ollama version 0.5.x
```
---
# 三、启动 Ollama 服务
检查状态:
```bash
systemctl status ollama
```
如果未运行:
```bash
sudo systemctl start ollama
```
开机启动:
```bash
sudo systemctl enable ollama
```
---
# 四、下载 Qwen2.5-Coder 7B
下载模型:
```bash
ollama pull qwen2.5-coder:7b
```
下载大小:
```
≈ 4.5GB
```
下载完成查看:
```bash
ollama list
```
示例:
```
NAME SIZE
qwen2.5-coder:7b 4.6 GB
```
---
# 五、运行模型
启动交互模式:
```bash
ollama run qwen2.5-coder:7b
```
终端将进入:
```
>>> Send a message (/? for help)
```
测试:
```
Write a Python script to monitor CPU usage
```
模型会生成代码。
---
# 六、通过 API 调用
Ollama 默认提供 REST API
```
http://localhost:11434
```
测试 API
```bash
curl http://localhost:11434/api/chat -d '{
"model": "qwen2.5-coder:7b",
"messages": [
{"role": "user", "content": "Write a bash script to backup a directory"}
]
}'
```
返回示例:
```json
{
"message": {
"role": "assistant",
"content": "Here is a bash backup script..."
}
}
```
---
# 七、Python 调用
安装 SDK
```bash
pip install ollama
```
示例代码:
```python
from ollama import chat
response = chat(
model="qwen2.5-coder:7b",
messages=[
{
"role": "user",
"content": "Write a Python script to parse a CSV file"
}
]
)
print(response["message"]["content"])
```
---
# 八、NodeJS 调用
安装 SDK
```bash
npm install ollama
```
示例:
```javascript
import ollama from 'ollama'
const response = await ollama.chat({
model: 'qwen2.5-coder:7b',
messages: [
{ role: 'user', content: 'Write a docker-compose for n8n and postgres' }
]
})
console.log(response.message.content)
```
---
# 九、开放远程 API推荐
默认只监听:
```
127.0.0.1
```
如果要给:
- n8n
- OpenClaw
- WebUI
- Agent
使用,需要修改。
编辑:
```
/etc/systemd/system/ollama.service
```
增加:
```
Environment="OLLAMA_HOST=0.0.0.0"
```
重新加载:
```bash
sudo systemctl daemon-reload
sudo systemctl restart ollama
```
访问:
```
http://服务器IP:11434
```
---
# 十、GPU 加速(可选)
检查 GPU
```bash
nvidia-smi
```
如果安装了 CUDA
Ollama 会 **自动使用 GPU**。
无需额外配置。
---
# 十一、模型管理
查看模型:
```bash
ollama list
```
删除模型:
```bash
ollama rm qwen2.5-coder:7b
```
更新模型:
```bash
ollama pull qwen2.5-coder:7b
```
---
# 十二、性能优化
如果机器较小:
推荐:
```
qwen2.5-coder:3b
```
下载:
```bash
ollama pull qwen2.5-coder:3b
```
---
# 十三、推荐搭配工具
如果你正在做 **AI automation / agent / coding**
建议搭配:
|工具|用途|
|---|---|
|Open WebUI|ChatGPT UI|
|n8n|AI automation|
|LangChain|Agent framework|
|OpenClaw|AI coding agent|
例如 OpenClaw 配置:
```
ollama/qwen2.5-coder:7b
```
---
# 十四、最简安装流程3条命令
如果你只想快速安装:
```bash
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5-coder:7b
ollama run qwen2.5-coder:7b
```
---
# 十五、一个很多人不知道的优化(非常重要)
如果你打算做:
- DevOps automation
- SQL Agent
- Kubernetes troubleshooting
- n8n workflow AI
强烈建议使用:
```
qwen2.5-coder:7b
```
原因:
- Tool usage能力强
- Shell / Python / SQL理解强
- Repo级代码理解
比普通 `qwen2.5:7b` **更适合工程任务**
---
如果需要,我可以再给你一份 **更高级部署指南**
**《Ubuntu 本地 AI StackOllama + Qwen2.5-Coder + OpenWebUI + n8n + Agent 自动化》**
会包含:
- 本地 ChatGPT UI
- AI Coding Agent
- 自动化工作流
- DevOps AI 助手
基本上是一套 **完整的本地 AI 基础设施(非常适合开发者)**

View File

@@ -1,5 +1,5 @@
---
title: [教學] ChatGPT 先做知識整理,再讓 Canva、 Gamma AI 輸出簡報
title: 教學 ChatGPT 先做知識整理,再讓 Canva、 Gamma AI 輸出簡報
source: https://www.playpcesor.com/2025/10/chatgpt-canva-gamma-ai.html
author: shenwei
published: 2025-10-26