Instructions to use Lockout/qwen3-4b-heretic-zimage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Lockout/qwen3-4b-heretic-zimage 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 Lockout/qwen3-4b-heretic-zimage
# Run inference directly in the terminal:
llama cli -hf Lockout/qwen3-4b-heretic-zimage

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lockout/qwen3-4b-heretic-zimage
# Run inference directly in the terminal:
llama cli -hf Lockout/qwen3-4b-heretic-zimage

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 Lockout/qwen3-4b-heretic-zimage
# Run inference directly in the terminal:
./llama-cli -hf Lockout/qwen3-4b-heretic-zimage

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 Lockout/qwen3-4b-heretic-zimage
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lockout/qwen3-4b-heretic-zimage

Use Docker

docker model run hf.co/Lockout/qwen3-4b-heretic-zimage

LM Studio
Jan
Ollama
How to use Lockout/qwen3-4b-heretic-zimage with Ollama:
```
ollama run hf.co/Lockout/qwen3-4b-heretic-zimage
```
Unsloth Desktop

How to use Lockout/qwen3-4b-heretic-zimage with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lockout/qwen3-4b-heretic-zimage

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": "Lockout/qwen3-4b-heretic-zimage"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Lockout/qwen3-4b-heretic-zimage with Docker Model Runner:
```
docker model run hf.co/Lockout/qwen3-4b-heretic-zimage
```

Lemonade

How to use Lockout/qwen3-4b-heretic-zimage with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lockout/qwen3-4b-heretic-zimage

Run and chat with the model

lemonade run user.qwen3-4b-heretic-zimage-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Lockout/qwen3-4b-heretic-zimage with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lockout/qwen3-4b-heretic-zimage

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 Lockout/qwen3-4b-heretic-zimage

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Lockout/qwen3-4b-heretic-zimage with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Lockout/qwen3-4b-heretic-zimage

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 "Lockout/qwen3-4b-heretic-zimage" \
  --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"

I ran the actual TE from z-image through heretic and did the standard dataset. The model is abliterated.

Other qwens can be interesting but not as exact as the trained encoder.

V2 Version from new heretic has lower KLD. You decide if it's better.

Downloads last month: 2,640

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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