Gemma4 Local Deploy Agent Skill
在本机 Mac 或 Apple Silicon 上部署 Gemma 4 12B。本地安装/升级 llama.cpp，下载 GGUF 量化模型，用 llama-server 暴露 OpenAI-compatible API，或用 Ollama 暴露本地模型服务；按用户需求在默认 Q4_K_M、64K/128K 长上下文、QAT Q4_0 @ 256K、左右对比演示之间选择，配置 tmux 后台运行，验证健康检查、问答接口、资源占用和常见故障。当用户说部署 Gemma 4、Gemma 4 12B、本地大模型、长上下文、QAT、量化、llama-server、Ollama、GGUF、Mac 本地模型服务时使用。
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/majiayu000/spellbook --skill gemma4-local-deployThe instruction itself
8 sections, as written by the authorGemma 4 12B 本地部署
把 Gemma 4 12B 的 GGUF 版本部署成本机模型服务。默认使用 llama.cpp / llama-server、Apple Metal、Q4_K_M 和 tmux，只监听 loopback；用户明确要求 QAT、256K、对比演示或 Ollama 时才切换路线。
Operating Contract
- Direct actions: 读取本机硬件、磁盘、端口、进程和模型缓存；在用户已要求本地部署时，安装或升级明确的软件包、下载选定模型、创建专用模型目录和 tmux 会话，并只绑定
127.0.0.1。 - Escalate before: 停止不属于本 Skill 的现有进程、覆盖已有模型或配置、删除用户数据、监听公网地址、改变防火墙，或下载用户未选择的大型模型变体。
- Evidence-backed pushback: 如果用户指定的模型标签、上下文、内存预算或本机能力与当前可验证状态冲突，先展示命令输出并提出可运行的 profile，不伪造支持状态。
- Feedback loop: 现状检查 → 选择并复述 profile → 执行一条部署路线 → 当前会话完成健康、模型和聊天验证 → 报告端点、资源与限制。
默认选择
- 默认模型仓库：
ggml-org/gemma-4-12B-it-GGUF - 默认量化：
Q4_K_M - 默认模型名：
gemma-4-12b-it - 默认端点：
http://127.0.0.1:8080 - 默认上下文：
32768 - 12B 长上下文：用户明确要求时选择
65536或131072 - QAT 仓库：
google/gemma-4-12B-it-qat-q4_0-gguf - QAT profile：
Q4_0、262144上下文 - 默认后台会话：
gemma4-12b - 默认关闭 thinking：
--reasoning off，避免 OpenAI API 的message.content为空 - Ollama：只在用户明确要求 Ollama 或需要 Ollama 生态时使用
QAT 是训练时模拟量化，不等于无损。关键任务仍要用当前会话的真实响应验证。用户明确要更高质量时，优先建议 Q6_K 或 Q8_0；除非用户接受更高内存和更慢加载，不默认使用 bf16。
Profile 选择
| Profile | 适用场景 | Model / quant | Context | Port / alias |
|---|---|---|---:|---|
| daily-q4km-32k | 默认日常聊天、编码、低风险本地 API | ggml-org/...:Q4_K_M | 32768 | 8080 / gemma-4-12b-it |
| long-q4km-128k | 明确需要更长上下文，但保留默认 GGUF 路线 | ggml-org/...:Q4_K_M | 65536 或 131072 | 8080 / gemma-4-12b-it |
| qat-q4_0-256k | 明确要求 QAT、Q4_0、256K 或低内存长上下文 | google/...qat-q4_0-gguf:Q4_0 | 262144 | 8080 / gemma-4-12b-it-qat-q4_0 |
| compare-32k-vs-256k | 录屏、演示或 A/B 比较资源与速度 | 左 Q4_K_M，右 QAT Q4_0 | 32768 + 262144 | 8080 + 8081 |
最终回复必须说明选定 profile、端口、上下文和选择依据。不要把 256K 当作日常默认值。
执行流程
1. 搜索并确认现状
先检查已有安装、进程、端口、缓存、硬件和磁盘，避免重复部署：
command -v llama-server || true
llama-server --version || true
tmux has-session -t gemma4-12b 2>/dev/null && tmux display-message -p -t gemma4-12b '#S #{pane_pid}' || true
lsof -nP -iTCP:8080 -sTCP:LISTEN || true
ls -lh "$HOME/Library/Caches/llama.cpp/"*gemma-4-12B-it*Q4_K_M*.gguf 2>/dev/null || true
find "$HOME/Library/Caches/llama.cpp" "$HOME/Models" \( -name '*gemma-4-12b-it-qat-q4_0*.gguf' -o -name '*gemma-4-12B-it-qat-q4_0*.gguf' \) 2>/dev/null || true
system_profiler SPHardwareDataType | sed -n '1,30p'
df -h "$HOME"
这些 || true 只用于允许“尚未安装/尚未运行”这一预期发现结果；必须展示实际输出，不能把查询失败描述成部署成功。
2. 执行一条部署路线
daily-q4km-32k、long-q4km-128k、qat-q4_0-256k或compare-32k-vs-256k：先读并执行 llama.cpp 部署路线。- 用户明确要求 Ollama：先读并执行 Ollama 部署路线。
- 不要同时混用两条路线，也不要在没有端口检查的情况下启动第二个服务。
3. 验证并报告
部署后必须读取并执行 验证、资源与排障。成功至少需要当前会话证明：
/health返回健康状态/v1/models或 Ollama 模型列表包含选定模型- 用户要求长上下文时，运行时报告实际
n_ctx - 一次聊天响应的正文非空
- 端点仍只监听预期的本机地址和端口
Final response shape
默认用中文回答，并包含：
- 实际 endpoint URL 和 model id
- 选定 profile、量化与上下文
- tmux/session 管理命令
- 当前会话的验证结果
- 实际资源摘要、失败项和限制
没有验证数据时写“未验证”，不能用计划值代替运行值。
Cross-check
部署计划涉及超出默认 profile 的模型、上下文或资源判断时，使用
agents/openai.yaml 做独立复核；复核不能替代当前会话的本机验证。
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How to use it
Take majiayu000/gemma4-local-deploy from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.