Skill: twinsgeeks/gemma-gemma3 (version 1.0.1)

--- name: gemma-gemma3 description: Gemma 3 by Google — run Gemma 3 (4B, 12B, 27B) across your local device fleet. Google's most capable open model with 128K context, strong coding, and multilingual support. Fleet-routed to the best available machine via Ollama Herd. Cross-platform (macOS, Linux, Windows). Zero cloud costs. version: 1.0.1 homepage: https://github.com/geeks-accelerator/ollama-herd metadata: {"openclaw":{"emoji":"gem","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin","linux","windows"]}} --- # Gemma 3 — Run Google's Open Models Across Your Fleet Gemma 3 is Google's most capable open-source LLM family. 128K context window, strong coding performance, multilingual support across 140+ languages. The fleet router picks the best device for every request — no manual load balancing. ## Supported Gemma models | Model | Parameters | Ollama name | Best for | |-------|-----------|-------------|----------| | **Gemma 3 27B** | 27B | `gemma3:27b` | Highest quality — rivals much larger models | | **Gemma 3 12B** | 12B | `gemma3:12b` | Balanced quality and speed | | **Gemma 3 4B** | 4B | `gemma3:4b` | Fast, runs on low-RAM devices | | **Gemma 3 1B** | 1B | `gemma3:1b` | Ultra-light, instant responses | | **CodeGemma 7B** | 7B | `codegemma` | Code-focused variant | ## Quick start ```bash pip install ollama-herd # PyPI: https://pypi.org/project/ollama-herd/ herd # start the router (port 11435) herd-node # run on each device — finds the router automatically ``` No models are downloaded during installation. Models are pulled on demand when a request arrives, or manually via the dashboard. All pulls require user confirmation. ## Use Gemma through the fleet ### OpenAI SDK (drop-in replacement) ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") # Gemma 3 27B for complex reasoning response = client.chat.completions.create( model="gemma3:27b", messages=[{"role": "user", "content": "Explain quantum entanglement to a 10-year-old"}], stream=True, ) for chunk in response: print(chunk.choices[0].delta.content or "", end="") ``` ### Code generation with CodeGemma ```python response = client.chat.completions.create( model="codegemma", messages=[{"role": "user", "content": "Write a binary search tree in Rust with insert, delete, and search"}], ) print(response.choices[0].message.content) ``` ### curl (Ollama format) ```bash # Gemma 3 27B curl http://localhost:11435/api/chat -d '{ "model": "gemma3:27b", "messages": [{"role": "user", "content": "Translate to Japanese: The weather is beautiful today"}], "stream": false }' ``` ### curl (OpenAI format) ```bash curl http://localhost:11435/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "gemma3:4b", "messages": [{"role": "user", "content": "Hello"}]}' ``` ## Which Gemma for your hardware > **Cross-platform:** These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms. | Device | RAM | Best Gemma model | |--------|-----|-----------------| | MacBook Air (8GB) | 8GB | `gemma3:1b` — instant responses | | Mac Mini (16GB) | 16GB | `gemma3:4b` — strong for its size | | Mac Mini (24GB) | 24GB | `gemma3:12b` — great balance | | MacBook Pro (36GB) | 36GB | `gemma3:27b` — full power | | Mac Studio (64GB+) | 64GB+ | `gemma3:27b` + `codegemma` simultaneously | ## Why Gemma locally - **128K context** — process entire codebases and long documents - **140+ languages** — multilingual without switching models - **Google quality, zero cost** — no per-token charges after hardware - **Privacy** — all data stays on your network - **Fleet routing** — multiple machines share the load ## Check what's running ```bash # Models loaded in memory curl -s http://localhost:11435/api/ps | python3 -m json.tool # Fleet health curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool ``` Web dashboard at `http://localhost:11435/dashboard` — live monitoring. ## Also available on this fleet ### Other LLMs Llama 3.3, Qwen 3.5, DeepSeek-V3, DeepSeek-R1, Phi 4, Mistral, Codestral — same endpoint. ### Image generation ```bash curl -o image.png http://localhost:11435/api/generate-image \ -d '{"model": "z-image-turbo", "prompt": "a gemstone catching light", "width": 1024, "height": 1024}' ``` ### Speech-to-text ```bash curl http://localhost:11435/api/transcribe -F "file=@meeting.wav" -F "model=qwen3-asr" ``` ### Embeddings ```bash curl http://localhost:11435/api/embed \ -d '{"model": "nomic-embed-text", "input": "Google Gemma open source language model"}' ``` ## Full documentation - [Agent Setup Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) - [API Reference](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/api-reference.md) ## Contribute Ollama Herd is open source (MIT). Stars, issues, and PRs welcome — from humans and AI agents alike: - [GitHub](https://github.com/geeks-accelerator/ollama-herd) — 444 tests, fully async, `CLAUDE.md` makes AI agents productive instantly - Found a bug? [Open an issue](https://github.com/geeks-accelerator/ollama-herd/issues) - Want to add a feature? Fork, branch, PR — the test suite runs in under 40 seconds ## Guardrails - **Model downloads require explicit user confirmation** — Gemma models range from 1GB (1B) to 16GB (27B). - **Model deletion requires explicit user confirmation.** - Never delete or modify files in `~/.fleet-manager/`. - No models are downloaded automatically — all pulls are user-initiated or require opt-in via `auto_pull`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)