Skill: twinsgeeks/llama-llama3 (version 1.0.1)

--- name: llama-llama3 description: Llama 3 by Meta — run Llama 3.3, Llama 3.2, and Llama 3.1 across your local device fleet. The most popular open-source LLM family routed to the best available machine. 8B for fast responses, 70B for quality, 405B for frontier performance. OpenAI-compatible API, Cross-platform (macOS, Linux, Windows). Zero cloud costs. version: 1.0.1 homepage: https://github.com/geeks-accelerator/ollama-herd metadata: {"openclaw":{"emoji":"llama","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin","linux","windows"]}} --- # Llama 3 — Run Meta's LLMs Across Your Local Fleet The Llama family is the most widely deployed open-source LLM. This skill routes Llama requests across your devices — the fleet picks the best machine for every request automatically. ## Supported Llama models | Model | Parameters | Ollama name | Best for | |-------|-----------|-------------|----------| | **Llama 3.3** | 70B | `llama3.3:70b` | Best overall — matches GPT-4o on most benchmarks | | **Llama 3.2** | 1B, 3B | `llama3.2:3b` | Fast responses on low-RAM devices | | **Llama 3.1** | 8B, 70B, 405B | `llama3.1:70b` | Proven workhorse, massive community | | **Llama 3** | 8B, 70B | `llama3:70b` | Original release, still widely used | ## 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 Llama through the fleet ### OpenAI SDK (drop-in replacement) ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") response = client.chat.completions.create( model="llama3.3:70b", messages=[{"role": "user", "content": "Explain transformer architecture"}], stream=True, ) for chunk in response: print(chunk.choices[0].delta.content or "", end="") ``` ### curl (Ollama format) ```bash curl http://localhost:11435/api/chat -d '{ "model": "llama3.3:70b", "messages": [{"role": "user", "content": "Write a Python quicksort"}], "stream": false }' ``` ### curl (OpenAI format) ```bash curl http://localhost:11435/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "llama3.2:3b", "messages": [{"role": "user", "content": "Hello"}]}' ``` ## Which Llama model 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. Pick the model that fits your available memory — smaller models work great for most tasks: | Model | Min RAM | Example hardware | |-------|---------|----------------| | `llama3.2:1b` | 2GB | Any Mac — even 8GB | | `llama3.2:3b` | 4GB | Mac Mini (16GB) | | `llama3:8b` | 8GB | Mac Mini (16GB) | | `llama3.3:70b` | 48GB | Mac Studio M4 Max (128GB) | | `llama3.1:405b` | 256GB+ | Mac Studio M4 Ultra (256GB) or distributed | The fleet router sends requests to the machine where the model is loaded. No manual routing needed. ## Why run Llama locally - **Free after hardware** — Meta's license allows commercial use with no per-token cost - **Privacy** — prompts and responses never leave your network - **No rate limits** — your hardware, your throughput - **Fleet routing** — multiple machines share the load automatically ## See what's running ```bash # Models loaded in memory right now curl -s http://localhost:11435/api/ps | python3 -m json.tool # All models available across the fleet curl -s http://localhost:11435/api/tags | python3 -m json.tool ``` ## Monitor Llama performance ```bash # Recent request traces — see latency, tokens, which node handled each request curl -s "http://localhost:11435/dashboard/api/traces?limit=10" | python3 -m json.tool # Fleet health — 15 automated checks curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool ``` Web dashboard at `http://localhost:11435/dashboard` — live view of all nodes, queues, and models. ## Also available on this fleet ### Other LLM models Qwen 3.5, DeepSeek-V3, DeepSeek-R1, Phi 4, Mistral, Gemma 3, Codestral — any Ollama model routes through the same endpoint. ### Image generation ```bash curl http://localhost:11435/api/generate-image \ -d '{"model": "z-image-turbo", "prompt": "a llama in the mountains", "width": 512, "height": 512}' ``` ### Speech-to-text ```bash curl http://localhost:11435/api/transcribe -F "file=@recording.wav" -F "model=qwen3-asr" ``` ### Embeddings ```bash curl http://localhost:11435/api/embed \ -d '{"model": "nomic-embed-text", "input": "Meta Llama open source language model"}' ``` ## Full documentation - [Agent Setup Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) — all 4 model types - [API Reference](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/api-reference.md) — complete endpoint docs ## Guardrails - **Model downloads require explicit user confirmation** — Llama models range from 1GB (1B) to 230GB+ (405B). Always confirm before pulling. - **Model deletion requires explicit user confirmation.** - Never delete or modify files in `~/.fleet-manager/`. - If a model is too large for available memory, suggest a smaller variant. - No models are downloaded automatically — all pulls are user-initiated or require opt-in via the `auto_pull` setting.


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