Instructions to use isox/Qwen3-14B-HomeAssistant-ru-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use isox/Qwen3-14B-HomeAssistant-ru-GGUF 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 isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf isox/Qwen3-14B-HomeAssistant-ru-GGUF: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 isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf isox/Qwen3-14B-HomeAssistant-ru-GGUF: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 isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M

Use Docker

docker model run hf.co/isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use isox/Qwen3-14B-HomeAssistant-ru-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "isox/Qwen3-14B-HomeAssistant-ru-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isox/Qwen3-14B-HomeAssistant-ru-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M

Ollama
How to use isox/Qwen3-14B-HomeAssistant-ru-GGUF with Ollama:
```
ollama run hf.co/isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M
```
Unsloth Desktop

How to use isox/Qwen3-14B-HomeAssistant-ru-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf isox/Qwen3-14B-HomeAssistant-ru-GGUF: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": "isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use isox/Qwen3-14B-HomeAssistant-ru-GGUF with Docker Model Runner:
```
docker model run hf.co/isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M
```

Lemonade

How to use isox/Qwen3-14B-HomeAssistant-ru-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-14B-HomeAssistant-ru-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use isox/Qwen3-14B-HomeAssistant-ru-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf isox/Qwen3-14B-HomeAssistant-ru-GGUF: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 isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use isox/Qwen3-14B-HomeAssistant-ru-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf isox/Qwen3-14B-HomeAssistant-ru-GGUF: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 "isox/Qwen3-14B-HomeAssistant-ru-GGUF: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"

Qwen3-14B HomeAssistant Russian (GGUF)

Fine-tuned Qwen3-14B for Russian-language Home Assistant voice control with native tool calling.

Quick Start

# Q8_0 — best quality, needs 18GB+ VRAM (A10 24GB, RTX 3090/4090)
ollama pull hf.co/isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q8_0

# Q4_K_M — good balance, needs 10GB+ VRAM (RTX 3060 12GB+)
ollama pull hf.co/isox/Qwen3-14B-HomeAssistant-ru-GGUF:Q4_K_M

Custom Modelfile (recommended)

The included Modelfile sets recommended parameters (32K context, tool calling template, optimized sampling):

# Download GGUF + Modelfile, then create with custom settings
ollama create qwen3-14b-ha-ru -f Modelfile

Recommended Ollama environment for NVIDIA A10 24GB:

OLLAMA_FLASH_ATTENTION=1
OLLAMA_KV_CACHE_TYPE=q8_0
OLLAMA_NUM_PARALLEL=1

Model Details

Parameter	Value
Base model	Qwen/Qwen3-14B
Method	QLoRA → LoRA merge → GGUF
Training data	214,339 examples (Russian smart home commands)
LoRA rank	64 (alpha=128)
Training	1 epoch, 3,350 steps, 43h on NVIDIA H100 SXM 80GB
Final loss	0.2178
LoRA adapter	isox/Qwen3-14B-LoRA-HomeAssistant-ru

Available Quantizations

File	Quant	Size	VRAM	Speed (A10)
`Qwen3-14B-HomeAssistant-ru-Q8_0.gguf`	Q8_0	15 GB	~18 GB	~29 tok/s
`Qwen3-14B-HomeAssistant-ru-Q4_K_M.gguf`	Q4_K_M	8.4 GB	~10 GB	~35 tok/s

Supported Tools (Home Assistant)

The model was trained on the following Home Assistant service tool calls:

HassTurnOn / HassTurnOff — turn devices on/off
HassSetTemperature — set thermostat/AC temperature
HassStartTimer — start timers
HassLightSet — set brightness/color
HassOpenCover / HassCloseCover — blinds/curtains
HassMediaPause / HassMediaNext / HassVolumeSet — media control
HassLockLock / HassLockUnlock — door locks
HassVacuumStart / HassVacuumReturnToBase — robot vacuum
HassGetState — query device state

Usage with Home Assistant

This model is designed for use with Home LLM integration. It responds to Russian voice commands and produces Qwen3 tool calls in the correct format.

Example

User: Включи свет в гостиной
Model: HassTurnOn(name="Свет в гостиной")

User: Поставь кондиционер на 22 градуса
Model: HassSetTemperature(name="Кондиционер", temperature=22)

User: Привет, как дела?
Model: Привет! Всё отлично, чем могу помочь?

Training Data

214,339 examples generated from Russian device/action piles covering:

13 assistant personas (formal, friendly, sarcastic, slang, etc.)
2,016 device names
1,569 response templates
1,330 specific action phrases
646 templated action patterns

Dataset: isox/home-assistant-russian-train (private)

Downloads last month: 57

GGUF

Model size

15B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

Model tree for isox/Qwen3-14B-HomeAssistant-ru-GGUF

Base model

Qwen/Qwen3-14B-Base

Finetuned

Qwen/Qwen3-14B

Quantized

(214)

this model