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plutoedge/PlutoLM-1.5B
PlutoLM-1.5B is a machine learning model from plutoedge. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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.
Downloads · 30 days
19
28% of all-time downloads
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.gguf986 MB · 100%
From the Hugging Face model README
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.
| 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) |
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"}]}
| 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 |
// 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"}
]}
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
| 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 |
Apache 2.0 — same as base model (Qwen2.5-1.5B-Instruct by Alibaba Cloud).
Built by Plutobot AI · Jakarta, Indonesia