Instructions to use saik0s/comfy_backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use saik0s/comfy_backup with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="saik0s/comfy_backup", filename="ComfyUI/models/text_encoders/gemma-3-12b-it-q2_k.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 saik0s/comfy_backup 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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q4_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q4_K_S
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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf saik0s/comfy_backup:Q4_K_S
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 saik0s/comfy_backup:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf saik0s/comfy_backup:Q4_K_S
Use Docker
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- LM Studio
- Jan
- Ollama
How to use saik0s/comfy_backup with Ollama:
ollama run hf.co/saik0s/comfy_backup:Q4_K_S
- Unsloth Studio
How to use saik0s/comfy_backup 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 saik0s/comfy_backup 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 saik0s/comfy_backup to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for saik0s/comfy_backup to start chatting
- Pi
How to use saik0s/comfy_backup with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
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": "saik0s/comfy_backup:Q4_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use saik0s/comfy_backup with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
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 saik0s/comfy_backup:Q4_K_S
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use saik0s/comfy_backup with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf saik0s/comfy_backup:Q4_K_S
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 "saik0s/comfy_backup:Q4_K_S" \ --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 saik0s/comfy_backup with Docker Model Runner:
docker model run hf.co/saik0s/comfy_backup:Q4_K_S
- Lemonade
How to use saik0s/comfy_backup with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saik0s/comfy_backup:Q4_K_S
Run and chat with the model
lemonade run user.comfy_backup-Q4_K_S
List all available models
lemonade list
| # ComfyUI - mxToolkit - Max Smirnov 2024 | |
| import nodes | |
| class AnyType(str): | |
| def __ne__(self, __value: object) -> bool: | |
| return False | |
| any = AnyType("*") | |
| class mxSeed: | |
| def INPUT_TYPES(s): | |
| return { | |
| "required": { | |
| "X": ("INT", {"default": 0, "min": 0, "max": 4294967296}), | |
| }, | |
| } | |
| RETURN_TYPES = ("INT",) | |
| RETURN_NAMES = ("X",) | |
| FUNCTION = "main" | |
| CATEGORY = 'utils/mxToolkit' | |
| def main(self, X,): | |
| return (X,) | |
| class mxStop: | |
| def INPUT_TYPES(s): | |
| return { | |
| "required": { | |
| "In": (any,), | |
| }, | |
| } | |
| def VALIDATE_INPUTS(s, **kwargs): | |
| return True | |
| RETURN_TYPES = (any,) | |
| FUNCTION = "main" | |
| CATEGORY = 'utils/mxToolkit' | |
| def main(self, In): | |
| out = In; | |
| nodes.interrupt_processing(); | |
| return (out,) | |
| class mxSlider: | |
| def INPUT_TYPES(s): | |
| return { | |
| "required": { | |
| "Xi": ("INT", {"default": 20, "min": -4294967296, "max": 4294967296}), | |
| "Xf": ("FLOAT", {"default": 20, "min": -4294967296, "max": 4294967296}), | |
| "isfloatX": ("INT", {"default": 0, "min": 0, "max": 1}), | |
| }, | |
| } | |
| RETURN_TYPES = (any,) | |
| RETURN_NAMES = ("X",) | |
| FUNCTION = "main" | |
| CATEGORY = 'utils/mxToolkit' | |
| def main(self, Xi, Xf, isfloatX): | |
| if isfloatX > 0: | |
| out = Xf | |
| else: | |
| out = Xi | |
| return (out,) | |
| class mxSlider2D: | |
| def INPUT_TYPES(s): | |
| return { | |
| "required": { | |
| "Xi": ("INT", {"default": 512, "min": -4294967296, "max": 4294967296}), | |
| "Xf": ("FLOAT", {"default": 512, "min": -4294967296, "max": 4294967296}), | |
| "Yi": ("INT", {"default": 512, "min": -4294967296, "max": 4294967296}), | |
| "Yf": ("FLOAT", {"default": 512, "min": -4294967296, "max": 4294967296}), | |
| "isfloatX": ("INT", {"default": 0, "min": 0, "max": 1}), | |
| "isfloatY": ("INT", {"default": 0, "min": 0, "max": 1}), | |
| }, | |
| } | |
| RETURN_TYPES = (any, any,) | |
| RETURN_NAMES = ("X","Y",) | |
| FUNCTION = "main" | |
| CATEGORY = 'utils/mxToolkit' | |
| def main(self, Xi, Xf, isfloatX, Yi, Yf, isfloatY): | |
| if isfloatX > 0: | |
| outX = Xf | |
| else: | |
| outX = Xi | |
| if isfloatY > 0: | |
| outY = Yf | |
| else: | |
| outY = Yi | |
| return (outX, outY,) | |
| NODE_CLASS_MAPPINGS = { | |
| "mxSeed": mxSeed, | |
| "mxStop": mxStop, | |
| "mxSlider": mxSlider, | |
| "mxSlider2D": mxSlider2D, | |
| } | |
| NODE_DISPLAY_NAME_MAPPINGS = { | |
| "mxSeed": "Seed", | |
| "mxStop": "Stop", | |
| "mxSlider": "Slider", | |
| "mxSlider2D": "Slider 2D", | |
| } |