README.md

---
license: gemma
base_model:
  - google/gemma-4-E2B-it
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - smart-home
  - intent-parsing
  - slot-filling
  - command-understanding
  - gemma
  - iflytek
  - edge
extra_gated_heading: Access Domux on Hugging Face
extra_gated_prompt: >-
  Domux is a derivative of Google's Gemma model and its weights are governed by
  the Gemma Terms of Use. To access the weights, you must review and agree to
  the Gemma Terms of Use and Prohibited Use Policy.
extra_gated_button_content: Acknowledge the Gemma Terms of Use
---

<div align="center">
  <h1>Domux</h1>
  <p><b>A lightweight, low-latency command understanding model for smart-home control.</b></p>

  <p>
    <a href="https://github.com/iflytek/domux"><img src="https://img.shields.io/badge/GitHub-Repo-181717?logo=github"></a>
    <a href="https://modelscope.cn/models/iflytek/domux"><img src="https://img.shields.io/badge/🔧%20ModelScope-Model-blue"></a>
    <a href="https://ai.google.dev/gemma/terms"><img src="https://img.shields.io/badge/License-Gemma%20Terms-green"></a>
    <img src="https://img.shields.io/badge/Inference-vLLM%20%7C%20SGLang-orange">
  </p>
</div>

---

Domux (`Domux-Gemma-4-E2B-it`) is a fine-tuned language model built on **Gemma-4-E2B-it**. It turns natural-language smart-home commands into structured, pipe-delimited slots. Training combines supervised fine-tuning (SFT) with reinforcement learning via Group Relative Policy Optimization (GRPO) and custom reward functions.

> 📦 **Code, training scripts, evaluation suite, full benchmark report and dataset** live in the GitHub repository: **[github.com/iflytek/domux](https://github.com/iflytek/domux)**.

## ✨ Key Features

- **Fast response** — Optimized for low-latency inference on edge devices and servers.
- **Structured slot output** — Parses free-form commands into a fixed 7-field pipe-delimited schema.
- **High accuracy** — 98.37% result accuracy with 100% format compliance, outperforming much larger models.
- **Lightweight base** — Built on the compact Gemma-4-E2B-it, suitable for on-device and edge deployment.
- **Multi-action support** — Handles compound commands that map to multiple slot lines.
- **Generalizes across devices** — Handles arbitrary device names within each category, not a fixed whitelist.

## 🎬 Output Format

The model outputs pipe-delimited slots with 7 fields. Use `*` for unspecified or don't-care fields.

```
action|device|attribute|value|unit|room|floor
```

### Basic Examples

| Input | Output |
| --- | --- |
| Turn on the living room light | `turnOn\|Light\|*\|*\|*\|Living Room\|*` |
| Set bedroom AC to 22 degrees | `set\|AC\|temperature\|22\|Celsius\|Bedroom\|*` |
| Close the curtains 20 percent | `adjustDown\|Curtain\|openness\|20\|Percent\|*\|*` |

### Complex Multi-Attribute Command

**Input:**
```
Turn on the Master Light in the Master Bedroom on the Second Floor,
set brightness to 80%, color temperature to 4000K, color to Blue, and mode to Reading.
```

**Output:**
```
turnOn|Light|*|*|*|Master Bedroom|Second Floor
set|Light|brightness|80|Percent|Master Bedroom|Second Floor
set|Light|colorTemperature|4000|Kelvin|Master Bedroom|Second Floor
set|Light|color|Blue|*|Master Bedroom|Second Floor
set|Light|mode|Reading|*|Master Bedroom|Second Floor
```

Full specification: [Output Format Documentation](https://github.com/iflytek/domux/blob/main/docs/output-spec.md).

## 🏠 Supported Control Capabilities

Domux does **not rely on a fixed device whitelist** — it handles diverse device names through semantic understanding.

| Device Type | Naming Examples | Controllable Attributes | Value Range |
| --- | --- | --- | --- |
| **Light** | Light, Strip Light, Spot Light, Desk Lamp | `brightness` / `color` / `colorTemperature` / `mode` | 0–100% / Blue, Red, Green… / 3000–6500 K / Reading, Romance, Soft… |
| **AC** | AC, AC 1 | `temperature` / `mode` / `windSpeed` | 16–30 °C / Cool, Heat, Dry, Fan, Auto / Low, Medium, High |
| **Curtain** / **Blind** | Curtain, Blind, Sheer Curtain | `position` | 0–100% |
| **Scene Mode** | Romantic Mode, Party Mode, Sleeping Mode | — | — |

**Actions**: `turnOn`, `turnOff`, `set`, `adjustUp`, `adjustDown`, `activate`, `deactivate`, `pause`.

**Spatial context**: rooms (Living Room, Bedroom, Kitchen, Majlis, Prayer Room…) and floors (Ground Floor, Upstairs, Downstairs…), including numbered variants.

## 📊 Benchmark

Evaluated on a comprehensive test set of **4,057 samples** across 4 dimensions (single intent, multi-intent, omitted attributes, non-standard naming), benchmarked against **11 mainstream models** including Qwen3.5 series (2B-27B), Gemma 4 series, and leading closed-source APIs (DeepSeek-V4, Claude Haiku 4.5, Gemini 3.5 Flash).

Result accuracy reaches **98.37%** with **100%** format compliance.

📄 Full technical report and benchmark charts: [GitHub repository](https://github.com/iflytek/domux#-benchmark).

The **test set** and **evaluation script** are open-sourced under [`eval/`](https://github.com/iflytek/domux/tree/main/eval) so you can reproduce the results or evaluate your own model.

## 🚀 Quick Start

### Hardware

The model runs in **BF16 precision** and requires **20GB+ of VRAM** for single-GPU deployment.

### Download

```bash
# Hugging Face
git lfs install
git clone https://huggingface.co/iFlytekOpenSource/Domux
```

### Inference with vLLM

```bash
pip install "vllm==0.22.0"
```

```python
from vllm import LLM, SamplingParams

llm = LLM(model="iFlytekOpenSource/Domux", dtype="bfloat16")
sampling = SamplingParams(temperature=0.0, max_tokens=256)

prompt = "Turn on the Master Light in the Master Bedroom on the Second Floor, set brightness to 80%, color temperature to 4000K, color to Blue, and mode to Reading."
output = llm.chat([{"role": "user", "content": prompt}], sampling)
print(output[0].outputs[0].text)

# Output:
# turnOn|Light|*|*|*|Master Bedroom|Second Floor
# set|Light|brightness|80|Percent|Master Bedroom|Second Floor
# set|Light|colorTemperature|4000|Kelvin|Master Bedroom|Second Floor
# set|Light|color|Blue|*|Master Bedroom|Second Floor
# set|Light|mode|Reading|*|Master Bedroom|Second Floor
```

### Serve as an OpenAI-compatible API

```bash
# vLLM
python -m vllm.entrypoints.openai.api_server \
  --model iFlytekOpenSource/Domux \
  --served-model-name domux \
  --host 0.0.0.0 --port 8000 \
  --dtype bfloat16 --max-model-len 2048 --gpu-memory-utilization 0.9
```

```bash
# SGLang
pip install "sglang[all]==0.5.12"
python -m sglang.launch_server \
  --model-path iFlytekOpenSource/Domux \
  --host 0.0.0.0 --port 8000 \
  --dtype bfloat16 --context-length 2048
```

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="domux",
    messages=[{"role": "user", "content": "Turn on the bedroom light and set brightness to 60%"}],
    temperature=0.0,
)
print(response.choices[0].message.content)
```

## 📄 License

The **model weights** are a derivative of Google's Gemma and are made available under, and your use of them is governed by, the **[Gemma Terms of Use](https://ai.google.dev/gemma/terms)** and the **[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy)**. "Gemma" is a trademark of Google LLC.

The accompanying **source code** (training scripts, reward plugins, evaluation tooling) in the [GitHub repository](https://github.com/iflytek/domux) is licensed under **Apache-2.0**.

## 🙏 Acknowledgments

- Base model: [Gemma](https://ai.google.dev/gemma)
- Training framework: [ModelScope-Swift](https://github.com/modelscope/swift)
- Experiment tracking: [SwanLab](https://swanlab.cn/)

## Citation

```bibtex
@misc{domux2026,
  title  = {Domux: A Lightweight Low-Latency Command Understanding Model for Smart-Home Control},
  author = {iFLYTEK CO., LTD.},
  year   = {2026},
  url    = {https://github.com/iflytek/domux}
}
```