Instructions to use LuckyOda/comfyui-carbonara-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use LuckyOda/comfyui-carbonara-bundle with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LuckyOda/comfyui-carbonara-bundle", filename="models/text_encoders/qwen-4b-zimage-heretic-q8.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LuckyOda/comfyui-carbonara-bundle 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 LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: llama cli -hf LuckyOda/comfyui-carbonara-bundle
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: llama cli -hf LuckyOda/comfyui-carbonara-bundle
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 LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: ./llama-cli -hf LuckyOda/comfyui-carbonara-bundle
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 LuckyOda/comfyui-carbonara-bundle # Run inference directly in the terminal: ./build/bin/llama-cli -hf LuckyOda/comfyui-carbonara-bundle
Use Docker
docker model run hf.co/LuckyOda/comfyui-carbonara-bundle
- LM Studio
- Jan
- Ollama
How to use LuckyOda/comfyui-carbonara-bundle with Ollama:
ollama run hf.co/LuckyOda/comfyui-carbonara-bundle
- Unsloth Studio
How to use LuckyOda/comfyui-carbonara-bundle with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LuckyOda/comfyui-carbonara-bundle to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LuckyOda/comfyui-carbonara-bundle to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LuckyOda/comfyui-carbonara-bundle to start chatting
- Pi
How to use LuckyOda/comfyui-carbonara-bundle with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LuckyOda/comfyui-carbonara-bundle
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "LuckyOda/comfyui-carbonara-bundle" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use LuckyOda/comfyui-carbonara-bundle with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LuckyOda/comfyui-carbonara-bundle
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 LuckyOda/comfyui-carbonara-bundle
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use LuckyOda/comfyui-carbonara-bundle with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LuckyOda/comfyui-carbonara-bundle
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 "LuckyOda/comfyui-carbonara-bundle" \ --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"
- Docker Model Runner
How to use LuckyOda/comfyui-carbonara-bundle with Docker Model Runner:
docker model run hf.co/LuckyOda/comfyui-carbonara-bundle
- Lemonade
How to use LuckyOda/comfyui-carbonara-bundle with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LuckyOda/comfyui-carbonara-bundle
Run and chat with the model
lemonade run user.comfyui-carbonara-bundle-{{QUANT_TAG}}List all available models
lemonade list
| def MakeSmartType(t): | |
| if isinstance(t, str): | |
| return SmartType(t) | |
| return t | |
| class SmartType(str): | |
| def __ne__(self, other): | |
| if self == "*" or other == "*": | |
| return False | |
| selfset = set(self.split(',')) | |
| otherset = set(other.split(',')) | |
| return not selfset.issubset(otherset) | |
| def VariantSupport(): | |
| def decorator(cls): | |
| if hasattr(cls, "INPUT_TYPES"): | |
| old_input_types = getattr(cls, "INPUT_TYPES") | |
| def new_input_types(*args, **kwargs): | |
| types = old_input_types(*args, **kwargs) | |
| for category in ["required", "optional"]: | |
| if category not in types: | |
| continue | |
| for key, value in types[category].items(): | |
| if isinstance(value, tuple): | |
| types[category][key] = (MakeSmartType(value[0]),) + value[1:] | |
| return types | |
| setattr(cls, "INPUT_TYPES", new_input_types) | |
| if hasattr(cls, "RETURN_TYPES"): | |
| old_return_types = cls.RETURN_TYPES | |
| setattr(cls, "RETURN_TYPES", tuple(MakeSmartType(x) for x in old_return_types)) | |
| if hasattr(cls, "VALIDATE_INPUTS"): | |
| # Reflection is used to determine what the function signature is, so we can't just change the function signature | |
| raise NotImplementedError("VariantSupport does not support VALIDATE_INPUTS yet") | |
| else: | |
| def validate_inputs(input_types): | |
| inputs = cls.INPUT_TYPES() | |
| for key, value in input_types.items(): | |
| if isinstance(value, SmartType): | |
| continue | |
| if "required" in inputs and key in inputs["required"]: | |
| expected_type = inputs["required"][key][0] | |
| elif "optional" in inputs and key in inputs["optional"]: | |
| expected_type = inputs["optional"][key][0] | |
| else: | |
| expected_type = None | |
| if expected_type is not None and MakeSmartType(value) != expected_type: | |
| return f"Invalid type of {key}: {value} (expected {expected_type})" | |
| return True | |
| setattr(cls, "VALIDATE_INPUTS", validate_inputs) | |
| return cls | |
| return decorator | |