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 / README.md

plutoedge

Upload README.md with huggingface_hub

c11c9f6 verified 11 days ago

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	---
	language:
	- en
	- id
	license: apache-2.0
	tags:
	- iot
	- edge-ai
	- raspberry-pi
	- ollama
	- gguf
	- lora
	- qwen2
	- plutoclaw
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	model-index:
	- name: PlutoEdge-1.5B
	results: []
	---

	# 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](https://github.com/plutoedge-dev/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

	```json
	// 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:
	```bash
	# 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):
	```bash
	# 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](https://github.com/plutoedge-dev/plutoclaw) for full GPIO automation:

	```bash
	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](https://plutobot.ai) · Jakarta, Indonesia