Skill: twinsgeeks/phi-phi4 (version 1.0.1)

--- name: phi-phi4 description: Phi 4 by Microsoft — small but powerful LLMs that run on minimal hardware. Phi-4 (14B), Phi-4-mini (3.8B), and Phi-3.5 across your device fleet. Perfect for low-RAM devices on any platform. State-of-the-art reasoning in a tiny footprint. Zero cloud costs. version: 1.0.1 homepage: https://github.com/geeks-accelerator/ollama-herd metadata: {"openclaw":{"emoji":"zap","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin","linux","windows"]}} --- # Phi 4 — Microsoft's Small Models, Big Results Phi models prove you don't need 70B parameters for great results. Phi-4 matches much larger models on reasoning benchmarks while running on hardware as modest as an 8GB MacBook Air. Route them across your fleet for even better throughput. ## Supported Phi models | Model | Parameters | Ollama name | RAM needed | Best for | |-------|-----------|-------------|-----------|----------| | **Phi-4** | 14B | `phi4` | 10GB | Reasoning, math, code — punches way above its weight | | **Phi-4-mini** | 3.8B | `phi4-mini` | 4GB | Ultra-fast on any device, even 8GB Macs | | **Phi-3.5-mini** | 3.8B | `phi3.5` | 4GB | Proven lightweight model | | **Phi-3-medium** | 14B | `phi3:14b` | 10GB | Balanced quality and speed | ## 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. All pulls require user confirmation. ## Why Phi for small devices A Mac Mini with 16GB RAM can run Phi-4 (14B) with room to spare. A MacBook Air with 8GB runs Phi-4-mini comfortably. These models start in seconds and respond fast — ideal for devices that can't load a 70B model. ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") # Phi-4 for reasoning response = client.chat.completions.create( model="phi4", messages=[{"role": "user", "content": "Solve: if 3x + 7 = 22, what is x?"}], ) print(response.choices[0].message.content) ``` ### Phi-4-mini — fastest response times ```bash curl http://localhost:11435/api/chat -d '{ "model": "phi4-mini", "messages": [{"role": "user", "content": "Summarize this in 3 bullet points: ..."}], "stream": false }' ``` ### OpenAI-compatible API ```bash curl http://localhost:11435/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model": "phi4", "messages": [{"role": "user", "content": "Write a unit test for a login function"}]}' ``` ## Ideal hardware pairings > **Cross-platform:** These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms. | Your device | RAM | Best Phi model | Why | |-------------|-----|---------------|-----| | MacBook Air (8GB) | 8GB | `phi4-mini` | Fits with room for other apps | | Mac Mini (16GB) | 16GB | `phi4` | Full Phi-4 with headroom | | Mac Mini (24GB) | 24GB | `phi4` | Can run Phi-4 + an embedding model simultaneously | | MacBook Pro (36GB) | 36GB | `phi4` + `phi4-mini` | Both loaded, router picks based on task | ## Monitor your fleet ```bash # What's loaded and where curl -s http://localhost:11435/api/ps | python3 -m json.tool # Fleet health overview curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool # Model recommendations based on your hardware curl -s http://localhost:11435/dashboard/api/recommendations | python3 -m json.tool ``` Web dashboard at `http://localhost:11435/dashboard` — live view of nodes, queues, and performance. ## Also available on this fleet ### Larger LLMs (when you need more power) Llama 3.3 (70B), Qwen 3.5, DeepSeek-R1, Mistral Large — route to a bigger machine in the fleet. ### Image generation ```bash curl http://localhost:11435/api/generate-image \ -d '{"model": "z-image-turbo", "prompt": "minimalist circuit board art", "width": 512, "height": 512}' ``` ### 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": "Microsoft Phi small 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** — Phi models are small (2-8GB) but still require confirmation. - **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.


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