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
- llama.cpp
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
- Pi
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"
Please Upload the Safetensors
Please Upload the Safetensors
I also also looking forwards to the safetensors as gguf isn't supported in many places. Also, full precision would be great. It doesn't have many parameters and the full model can fit into VRAM without a problem.
Ok, I can give you what I got (BF16) but my upload is shit. I also wanted to experiment with larger dataset and the grimjim ablation. Same with picking the next one down of 3 refusals but much lower KLD. Full model is 8gb, on my internet that's I think a 2hr upload or more.
RESOLVED BY UPDATING THE GGUF NODES.
Gives an error in ComfyUI. Is this supposed to be a Clip Text Encoder?
Works with the GGUF clip text encoder node but it's an LLM which passes embeddings to DIT.