Instructions to use plutoedge/PlutoEdge-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use plutoedge/PlutoEdge-1.5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="plutoedge/PlutoEdge-1.5B",
	filename="PlutoEdge-1.5B-v4-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use plutoedge/PlutoEdge-1.5B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf plutoedge/PlutoEdge-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf plutoedge/PlutoEdge-1.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf plutoedge/PlutoEdge-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf plutoedge/PlutoEdge-1.5B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf plutoedge/PlutoEdge-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf plutoedge/PlutoEdge-1.5B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf plutoedge/PlutoEdge-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf plutoedge/PlutoEdge-1.5B:Q4_K_M

Use Docker

docker model run hf.co/plutoedge/PlutoEdge-1.5B:Q4_K_M

LM Studio
Jan
Ollama
How to use plutoedge/PlutoEdge-1.5B with Ollama:
```
ollama run hf.co/plutoedge/PlutoEdge-1.5B:Q4_K_M
```

Unsloth Studio

How to use plutoedge/PlutoEdge-1.5B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for plutoedge/PlutoEdge-1.5B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for plutoedge/PlutoEdge-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for plutoedge/PlutoEdge-1.5B to start chatting

How to use plutoedge/PlutoEdge-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf plutoedge/PlutoEdge-1.5B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "plutoedge/PlutoEdge-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use plutoedge/PlutoEdge-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf plutoedge/PlutoEdge-1.5B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default plutoedge/PlutoEdge-1.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use plutoedge/PlutoEdge-1.5B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf plutoedge/PlutoEdge-1.5B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "plutoedge/PlutoEdge-1.5B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use plutoedge/PlutoEdge-1.5B with Docker Model Runner:
```
docker model run hf.co/plutoedge/PlutoEdge-1.5B:Q4_K_M
```

Lemonade

How to use plutoedge/PlutoEdge-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull plutoedge/PlutoEdge-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.PlutoEdge-1.5B-Q4_K_M

List all available models

lemonade list

PlutoEdge-1.5B

PlutoEdge-1.5B is a domain-specific LLM fine-tuned for IoT edge automation, running on Raspberry Pi via Ollama. It powers PlutoClaw — an open-source Edge AI orchestrator for physical hardware control.

Runs fully offline on Raspberry Pi CPU. No GPU, no cloud, no API keys.

Model Details

Property	Value
Base model	Qwen2.5-1.5B-Instruct
Fine-tuning	MLX LoRA (rank=16, 1500 iters)
Format	GGUF Q4_K_M
Size	~940 MB
Raspberry Pi inference	~37s / response (CPU)
Context window	1024 tokens (Pi) / 2048 tokens (Mac)
Training samples	759 (synthetic + acon96/Home-Assistant-Requests)
Language	English (Bahasa Indonesia input supported via normalization)

What It Does

PlutoEdge understands IoT control commands and responds with structured PLUTO_ACTION JSON that PlutoClaw executes on GPIO hardware:

User:  "Turn on the ventilation fan"
Pluto: "Turning on the ventilation fan now."
       PLUTO_ACTION: {"type": "actuator_trigger", "params": {"id": "relay1", "action": "on"}}

User:  "Worker detected without hard hat"
Pluto: "PPE violation detected. Sounding alert buzzer."
       PLUTO_ACTION: {"type": "multi_trigger", "params": [{"id": "buzzer1", "action": "pulse"}, {"id": "led1", "action": "on"}]}

Training Domains

Domain	Samples	Skills
Smart Home	520	relay control, automation, flood/fire detection
Knowledge Q&A	66	PlutoClaw platform, skill selection, setup
Warehouse	41	ppe_guard, intrusion, forklift_guard
Sustainability	34	solar/grid, carbon footprint, water monitoring
Poultry Farming	33	coop_monitor, sick_animal, animal_count
Industrial	30	predictive_maintenance, quality_control
Agriculture	27	irrigation_control, crop_monitor

PLUTO_ACTION Format

// Single device
PLUTO_ACTION: {"type": "actuator_trigger", "params": {"id": "relay1", "action": "on"}}

// Multiple devices simultaneously
PLUTO_ACTION: {"type": "multi_trigger", "params": [
  {"id": "relay1", "action": "off"},
  {"id": "buzzer1", "action": "on"},
  {"id": "led1", "action": "on"}
]}

Quickstart with Ollama

Option 1 — Pull directly from HuggingFace:

# Install Ollama on Raspberry Pi
curl -fsSL https://ollama.ai/install.sh | sh

# Pull and run PlutoEdge
ollama pull hf.co/plutoedge/PlutoEdge-1.5B
ollama run hf.co/plutoedge/PlutoEdge-1.5B

Option 2 — Build from PlutoClaw repo (recommended for full GPIO automation):

# Install Ollama on Raspberry Pi
curl -fsSL https://ollama.ai/install.sh | sh

# Clone PlutoClaw and register PlutoEdge locally
git clone https://github.com/plutoedge-dev/plutoclaw.git
cd plutoclaw/models/PlutoEdge-1.5B-v4
ollama create plutoedge -f Modelfile

Or use with PlutoClaw for full GPIO automation:

git clone https://github.com/plutoedge-dev/plutoclaw.git
cd plutoclaw
pip install -r requirements.txt
# Edit config.yaml, then:
python3 main.py

Files

File	Description
`PlutoEdge-1.5B-v4-Q4_K_M.gguf`	Quantized model for Raspberry Pi (940 MB)
`PlutoEdge-1.5B-v4-F16.gguf`	Full precision GGUF (3.1 GB)
`Modelfile`	Ollama Modelfile with system prompt

License

Apache 2.0 — same as base model (Qwen2.5-1.5B-Instruct by Alibaba Cloud).

Built by Plutobot AI · Jakarta, Indonesia

Downloads last month: 38

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for plutoedge/PlutoEdge-1.5B

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

(1173)

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