Instructions to use JoyFusionAI/gguf-components 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 JoyFusionAI/gguf-components 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 JoyFusionAI/gguf-components:Q8_0 # Run inference directly in the terminal: llama cli -hf JoyFusionAI/gguf-components:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf JoyFusionAI/gguf-components:Q8_0 # Run inference directly in the terminal: llama cli -hf JoyFusionAI/gguf-components:Q8_0
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 JoyFusionAI/gguf-components:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf JoyFusionAI/gguf-components:Q8_0
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 JoyFusionAI/gguf-components:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf JoyFusionAI/gguf-components:Q8_0
Use Docker
docker model run hf.co/JoyFusionAI/gguf-components:Q8_0
- LM Studio
- Jan
- Ollama
How to use JoyFusionAI/gguf-components with Ollama:
ollama run hf.co/JoyFusionAI/gguf-components:Q8_0
- Unsloth Desktop
- Pi
How to use JoyFusionAI/gguf-components with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JoyFusionAI/gguf-components:Q8_0
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": "JoyFusionAI/gguf-components:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use JoyFusionAI/gguf-components with Docker Model Runner:
docker model run hf.co/JoyFusionAI/gguf-components:Q8_0
- Lemonade
How to use JoyFusionAI/gguf-components with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull JoyFusionAI/gguf-components:Q8_0
Run and chat with the model
lemonade run user.gguf-components-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use JoyFusionAI/gguf-components with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JoyFusionAI/gguf-components:Q8_0
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 JoyFusionAI/gguf-components:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use JoyFusionAI/gguf-components with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf JoyFusionAI/gguf-components:Q8_0
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 "JoyFusionAI/gguf-components:Q8_0" \ --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"
gguf-components
Shared text encoders and VAEs, in the formats stable-diffusion.cpp loads directly, used by JoyFusion's Android on-device inference. All files are byte-identical mirrors of their sources.
| File | Used by | Source | Size | SHA-256 |
|---|---|---|---|---|
Qwen3-4B-Instruct-2507-Q4_0.gguf |
Z-Image Turbo (text encoder) | unsloth/Qwen3-4B-Instruct-2507-GGUF | 2,375,773,280 | e0ba675d86ab277c61701c6793659b2ae801d95e3be791464c321e6fbf613be2 |
Qwen3-4B-Instruct-2507-Q8_0.gguf |
Z-Image Turbo (text encoder) | same | 4,280,405,600 | 391c1e410fd9f4cf2de2b510273b56a84c19ce18f4fa3bfb3774031dac4ef068 |
Qwen3-4B-Q4_0.gguf |
FLUX.2 klein 4B (text encoder) | unsloth/Qwen3-4B-GGUF | 2,375,773,472 | 92f4a83cdfe691b216a949f915e47f35f0ed85b833aa03490c719160ac5cdf7a |
Qwen3-4B-Q8_0.gguf |
FLUX.2 klein 4B (text encoder) | same | 4,280,405,792 | eed555233267a33c7e8ee31682762cc7751b3f6d224039086e0e846f05fffa5d |
Qwen3-0.6B-Base.Q8_0.gguf |
Anima (text encoder) | mradermacher/Qwen3-0.6B-Base-GGUF | 639,447,232 | 4b088f1793f6cba9f0c2f77ab835ef6734f205c2159168698c6e1a51b7df168a |
ae.safetensors |
Z-Image (FLUX.1 VAE) | Comfy-Org/z_image_turbo | 335,304,388 | afc8e28272cd15db3919bacdb6918ce9c1ed22e96cb12c4d5ed0fba823529e38 |
flux2-vae.safetensors |
FLUX.2 klein (FLUX.2 VAE) | Comfy-Org/vae-text-encorder-for-flux-klein-4b | 336,211,292 | 868fe7b343cc8f3a19dbcfcafbc3d5f888802be3f89bd81b65b3621a066ce8f3 |
qwen_image_vae.safetensors |
Anima (Qwen-Image VAE) | circlestone-labs/Anima | 253,806,246 | a70580f0213e67967ee9c95f05bb400e8fb08307e017a924bf3441223e023d1f |
License
Qwen3 (Alibaba), the FLUX.1 / FLUX.2 VAEs (Black Forest Labs) and the Qwen-Image VAE (Alibaba) are all released under the
Apache License 2.0 (see LICENSE.md).
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