Instructions to use infohound/ha-voice-7b-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 infohound/ha-voice-7b-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 infohound/ha-voice-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf infohound/ha-voice-7b-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 infohound/ha-voice-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf infohound/ha-voice-7b-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 infohound/ha-voice-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf infohound/ha-voice-7b-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 infohound/ha-voice-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf infohound/ha-voice-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/infohound/ha-voice-7b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use infohound/ha-voice-7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "infohound/ha-voice-7b-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": "infohound/ha-voice-7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/infohound/ha-voice-7b-GGUF:Q4_K_M

Ollama
How to use infohound/ha-voice-7b-GGUF with Ollama:
```
ollama run hf.co/infohound/ha-voice-7b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use infohound/ha-voice-7b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf infohound/ha-voice-7b-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": "infohound/ha-voice-7b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use infohound/ha-voice-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/infohound/ha-voice-7b-GGUF:Q4_K_M
```

Lemonade

How to use infohound/ha-voice-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull infohound/ha-voice-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ha-voice-7b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use infohound/ha-voice-7b-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 infohound/ha-voice-7b-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 infohound/ha-voice-7b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use infohound/ha-voice-7b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf infohound/ha-voice-7b-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 "infohound/ha-voice-7b-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"

⚠️ DEPRECATED — use `infohound/ha-voice-granite-3b-GGUF` instead

This model is no longer maintained. It has been replaced by ha-voice-granite-3b-GGUF, which is better on every axis that matters for Home Assistant voice control.

	this (deprecated)	replacement
Base	Qwen2.5-7B-Instruct	IBM Granite 4.1 3B
Quantization	Q4_K_M (~4.5 BPW)	Q8_0 (8.5 BPW)
Size	4.7 GB	3.6 GB
Latency	341 ms	240 ms
Context	8,192	65,536

Why you should switch

This model has two failure modes that were the reason for the replacement:

It frequently returns no speech after a tool call. Home Assistant then raises Last content in chat log is not an AssistantContent and the user hears "Unable to get response" — even though the action actually succeeded. Deployments had to patch around it with a synthetic "Done" response.
It goes silent entirely above ~1,200 prompt tokens — no content and no tool call. Real Home Assistant prompts are ~2,900 tokens, squarely inside the failing range. This is a capability limit, not a context limit: no num_ctx value fixes it.

The replacement was trained and validated specifically against both, and measures 0 missing responses in 72 tool-call round trips and 0 silent responses in 84 trials at production prompt size.

The files here are left in place so existing pulls do not break, but they will not be updated.

Downloads last month: 11

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for infohound/ha-voice-7b-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(413)

this model

⚠️ DEPRECATED — use infohound/ha-voice-granite-3b-GGUF instead

Why you should switch

Model tree for infohound/ha-voice-7b-GGUF

⚠️ DEPRECATED — use `infohound/ha-voice-granite-3b-GGUF` instead