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
- 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
| import math | |
| from typing import Any, Callable, Mapping | |
| DEFAULT_FLOAT = ("FLOAT", {"default": 0.0, "step": 0.001, "round": False}) | |
| FLOAT_UNARY_OPERATIONS: Mapping[str, Callable[[float], float]] = { | |
| "Neg": lambda a: -a, | |
| "Inc": lambda a: a + 1, | |
| "Dec": lambda a: a - 1, | |
| "Abs": lambda a: abs(a), | |
| "Sqr": lambda a: a * a, | |
| "Cube": lambda a: a * a * a, | |
| "Sqrt": lambda a: math.sqrt(a), | |
| "Exp": lambda a: math.exp(a), | |
| "Ln": lambda a: math.log(a), | |
| "Log10": lambda a: math.log10(a), | |
| "Log2": lambda a: math.log2(a), | |
| "Sin": lambda a: math.sin(a), | |
| "Cos": lambda a: math.cos(a), | |
| "Tan": lambda a: math.tan(a), | |
| "Asin": lambda a: math.asin(a), | |
| "Acos": lambda a: math.acos(a), | |
| "Atan": lambda a: math.atan(a), | |
| "Sinh": lambda a: math.sinh(a), | |
| "Cosh": lambda a: math.cosh(a), | |
| "Tanh": lambda a: math.tanh(a), | |
| "Asinh": lambda a: math.asinh(a), | |
| "Acosh": lambda a: math.acosh(a), | |
| "Atanh": lambda a: math.atanh(a), | |
| "Round": lambda a: round(a), | |
| "Floor": lambda a: math.floor(a), | |
| "Ceil": lambda a: math.ceil(a), | |
| "Trunc": lambda a: math.trunc(a), | |
| "Erf": lambda a: math.erf(a), | |
| "Erfc": lambda a: math.erfc(a), | |
| "Gamma": lambda a: math.gamma(a), | |
| "Radians": lambda a: math.radians(a), | |
| "Degrees": lambda a: math.degrees(a), | |
| } | |
| FLOAT_UNARY_CONDITIONS: Mapping[str, Callable[[float], bool]] = { | |
| "IsZero": lambda a: a == 0.0, | |
| "IsPositive": lambda a: a > 0.0, | |
| "IsNegative": lambda a: a < 0.0, | |
| "IsNonZero": lambda a: a != 0.0, | |
| "IsPositiveInfinity": lambda a: math.isinf(a) and a > 0.0, | |
| "IsNegativeInfinity": lambda a: math.isinf(a) and a < 0.0, | |
| "IsNaN": lambda a: math.isnan(a), | |
| "IsFinite": lambda a: math.isfinite(a), | |
| "IsInfinite": lambda a: math.isinf(a), | |
| "IsEven": lambda a: a % 2 == 0.0, | |
| "IsOdd": lambda a: a % 2 != 0.0, | |
| } | |
| FLOAT_BINARY_OPERATIONS: Mapping[str, Callable[[float, float], float]] = { | |
| "Add": lambda a, b: a + b, | |
| "Sub": lambda a, b: a - b, | |
| "Mul": lambda a, b: a * b, | |
| "Div": lambda a, b: a / b, | |
| "Mod": lambda a, b: a % b, | |
| "Pow": lambda a, b: a**b, | |
| "FloorDiv": lambda a, b: a // b, | |
| "Max": lambda a, b: max(a, b), | |
| "Min": lambda a, b: min(a, b), | |
| "Log": lambda a, b: math.log(a, b), | |
| "Atan2": lambda a, b: math.atan2(a, b), | |
| } | |
| FLOAT_BINARY_CONDITIONS: Mapping[str, Callable[[float, float], bool]] = { | |
| "Eq": lambda a, b: a == b, | |
| "Neq": lambda a, b: a != b, | |
| "Gt": lambda a, b: a > b, | |
| "Gte": lambda a, b: a >= b, | |
| "Lt": lambda a, b: a < b, | |
| "Lte": lambda a, b: a <= b, | |
| } | |
| class FloatUnaryOperation: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "op": (list(FLOAT_UNARY_OPERATIONS.keys()),), | |
| "a": DEFAULT_FLOAT, | |
| } | |
| } | |
| RETURN_TYPES = ("FLOAT",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/float" | |
| def op(self, op: str, a: float) -> tuple[float]: | |
| return (FLOAT_UNARY_OPERATIONS[op](a),) | |
| class FloatUnaryCondition: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "op": (list(FLOAT_UNARY_CONDITIONS.keys()),), | |
| "a": DEFAULT_FLOAT, | |
| } | |
| } | |
| RETURN_TYPES = ("BOOLEAN",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/float" | |
| def op(self, op: str, a: float) -> tuple[bool]: | |
| return (FLOAT_UNARY_CONDITIONS[op](a),) | |
| class FloatBinaryOperation: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "op": (list(FLOAT_BINARY_OPERATIONS.keys()),), | |
| "a": DEFAULT_FLOAT, | |
| "b": DEFAULT_FLOAT, | |
| } | |
| } | |
| RETURN_TYPES = ("FLOAT",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/float" | |
| def op(self, op: str, a: float, b: float) -> tuple[float]: | |
| return (FLOAT_BINARY_OPERATIONS[op](a, b),) | |
| class FloatBinaryCondition: | |
| def INPUT_TYPES(cls) -> Mapping[str, Any]: | |
| return { | |
| "required": { | |
| "op": (list(FLOAT_BINARY_CONDITIONS.keys()),), | |
| "a": DEFAULT_FLOAT, | |
| "b": DEFAULT_FLOAT, | |
| } | |
| } | |
| RETURN_TYPES = ("BOOLEAN",) | |
| FUNCTION = "op" | |
| CATEGORY = "math/float" | |
| def op(self, op: str, a: float, b: float) -> tuple[bool]: | |
| return (FLOAT_BINARY_CONDITIONS[op](a, b),) | |
| NODE_CLASS_MAPPINGS = { | |
| "CM_FloatUnaryOperation": FloatUnaryOperation, | |
| "CM_FloatUnaryCondition": FloatUnaryCondition, | |
| "CM_FloatBinaryOperation": FloatBinaryOperation, | |
| "CM_FloatBinaryCondition": FloatBinaryCondition, | |
| } | |