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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use plutoedge/PlutoLM-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/PlutoLM-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf plutoedge/PlutoLM-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/PlutoLM-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf plutoedge/PlutoLM-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/PlutoLM-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf plutoedge/PlutoLM-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/PlutoLM-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf plutoedge/PlutoLM-1.5B:Q4_K_M

Use Docker

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

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

How to use plutoedge/PlutoLM-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/PlutoLM-1.5B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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/PlutoLM-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.PlutoLM-1.5B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use plutoedge/PlutoLM-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/PlutoLM-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/PlutoLM-1.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use plutoedge/PlutoLM-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/PlutoLM-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/PlutoLM-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"

plutoedge commited on Jun 27

Commit

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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 4B 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 |
+| **Pi 4B inference** | ~37s / response |
+| **Pi 5 inference** | ~18s / response |
+| **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
+```bash
+# Install Ollama on Raspberry Pi
+curl -fsSL https://ollama.ai/install.sh | sh
+# Pull and run PlutoEdge
+ollama pull plutoedge/PlutoEdge-1.5B-GGUF
+ollama run plutoedge/PlutoEdge-1.5B-GGUF
+```
+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