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 re | |
| import torch | |
| class TestIntConditions: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "a": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), | |
| "b": ("INT", {"default": 0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 1}), | |
| "operation": (["==", "!=", "<", ">", "<=", ">="],), | |
| }, | |
| } | |
| RETURN_TYPES = ("BOOLEAN",) | |
| FUNCTION = "int_condition" | |
| CATEGORY = "Testing/Logic" | |
| def int_condition(self, a, b, operation): | |
| if operation == "==": | |
| return (a == b,) | |
| elif operation == "!=": | |
| return (a != b,) | |
| elif operation == "<": | |
| return (a < b,) | |
| elif operation == ">": | |
| return (a > b,) | |
| elif operation == "<=": | |
| return (a <= b,) | |
| elif operation == ">=": | |
| return (a >= b,) | |
| class TestFloatConditions: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "a": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 1}), | |
| "b": ("FLOAT", {"default": 0, "min": -999999999999.0, "max": 999999999999.0, "step": 1}), | |
| "operation": (["==", "!=", "<", ">", "<=", ">="],), | |
| }, | |
| } | |
| RETURN_TYPES = ("BOOLEAN",) | |
| FUNCTION = "float_condition" | |
| CATEGORY = "Testing/Logic" | |
| def float_condition(self, a, b, operation): | |
| if operation == "==": | |
| return (a == b,) | |
| elif operation == "!=": | |
| return (a != b,) | |
| elif operation == "<": | |
| return (a < b,) | |
| elif operation == ">": | |
| return (a > b,) | |
| elif operation == "<=": | |
| return (a <= b,) | |
| elif operation == ">=": | |
| return (a >= b,) | |
| class TestStringConditions: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "a": ("STRING", {"multiline": False}), | |
| "b": ("STRING", {"multiline": False}), | |
| "operation": (["a == b", "a != b", "a IN b", "a MATCH REGEX(b)", "a BEGINSWITH b", "a ENDSWITH b"],), | |
| "case_sensitive": ("BOOLEAN", {"default": True}), | |
| }, | |
| } | |
| RETURN_TYPES = ("BOOLEAN",) | |
| FUNCTION = "string_condition" | |
| CATEGORY = "Testing/Logic" | |
| def string_condition(self, a, b, operation, case_sensitive): | |
| if not case_sensitive: | |
| a = a.lower() | |
| b = b.lower() | |
| if operation == "a == b": | |
| return (a == b,) | |
| elif operation == "a != b": | |
| return (a != b,) | |
| elif operation == "a IN b": | |
| return (a in b,) | |
| elif operation == "a MATCH REGEX(b)": | |
| try: | |
| return (re.match(b, a) is not None,) | |
| except: | |
| return (False,) | |
| elif operation == "a BEGINSWITH b": | |
| return (a.startswith(b),) | |
| elif operation == "a ENDSWITH b": | |
| return (a.endswith(b),) | |
| class TestToBoolNode: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "value": ("*",), | |
| }, | |
| "optional": { | |
| "invert": ("BOOLEAN", {"default": False}), | |
| }, | |
| } | |
| RETURN_TYPES = ("BOOLEAN",) | |
| FUNCTION = "to_bool" | |
| CATEGORY = "Testing/Logic" | |
| def to_bool(self, value, invert = False): | |
| if isinstance(value, torch.Tensor): | |
| if value.max().item() == 0 and value.min().item() == 0: | |
| result = False | |
| else: | |
| result = True | |
| else: | |
| try: | |
| result = bool(value) | |
| except: | |
| # Can't convert it? Well then it's something or other. I dunno, I'm not a Python programmer. | |
| result = True | |
| if invert: | |
| result = not result | |
| return (result,) | |
| class TestBoolOperationNode: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "a": ("BOOLEAN",), | |
| "b": ("BOOLEAN",), | |
| "op": (["a AND b", "a OR b", "a XOR b", "NOT a"],), | |
| }, | |
| } | |
| RETURN_TYPES = ("BOOLEAN",) | |
| FUNCTION = "bool_operation" | |
| CATEGORY = "Testing/Logic" | |
| def bool_operation(self, a, b, op): | |
| if op == "a AND b": | |
| return (a and b,) | |
| elif op == "a OR b": | |
| return (a or b,) | |
| elif op == "a XOR b": | |
| return (a ^ b,) | |
| elif op == "NOT a": | |
| return (not a,) | |
| CONDITION_NODE_CLASS_MAPPINGS = { | |
| "TestIntConditions": TestIntConditions, | |
| "TestFloatConditions": TestFloatConditions, | |
| "TestStringConditions": TestStringConditions, | |
| "TestToBoolNode": TestToBoolNode, | |
| "TestBoolOperationNode": TestBoolOperationNode, | |
| } | |
| CONDITION_NODE_DISPLAY_NAME_MAPPINGS = { | |
| "TestIntConditions": "Int Condition", | |
| "TestFloatConditions": "Float Condition", | |
| "TestStringConditions": "String Condition", | |
| "TestToBoolNode": "To Bool", | |
| "TestBoolOperationNode": "Bool Operation", | |
| } | |