- 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
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 automaticallyNo 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.
from openai import OpenAI
client = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed")
# 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
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
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
# 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.toolWeb 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
curl http://localhost:11435/api/generate-image \
-d '{"model": "z-image-turbo", "prompt": "minimalist circuit board art", "width": 512, "height": 512}'Speech-to-text
curl http://localhost:11435/api/transcribe -F "file=@meeting.wav" -F "model=qwen3-asr"Embeddings
curl http://localhost:11435/api/embed \
-d '{"model": "nomic-embed-text", "input": "Microsoft Phi small language model"}'Full documentation
- Agent Setup Guide — all 4 model types
- API Reference — 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.