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
| from comfy_api.latest import IO | |
| def validate_node_input( | |
| received_type: str, input_type: str, strict: bool = False | |
| ) -> bool: | |
| """ | |
| received_type and input_type are both strings of the form "T1,T2,...". | |
| If strict is True, the input_type must contain the received_type. | |
| For example, if received_type is "STRING" and input_type is "STRING,INT", | |
| this will return True. But if received_type is "STRING,INT" and input_type is | |
| "INT", this will return False. | |
| If strict is False, the input_type must have overlap with the received_type. | |
| For example, if received_type is "STRING,BOOLEAN" and input_type is "STRING,INT", | |
| this will return True. | |
| Supports pre-union type extension behaviour of ``__ne__`` overrides. | |
| """ | |
| # If the types are exactly the same, we can return immediately | |
| # Use pre-union behaviour: inverse of `__ne__` | |
| # NOTE: this lets legacy '*' Any types work that override the __ne__ method of the str class. | |
| if not received_type != input_type: | |
| return True | |
| # If one of the types is '*', we can return True immediately; this is the 'Any' type. | |
| if received_type == IO.AnyType.io_type or input_type == IO.AnyType.io_type: | |
| return True | |
| # If the received type or input_type is a MatchType, we can return True immediately; | |
| # validation for this is handled by the frontend | |
| if received_type == IO.MatchType.io_type or input_type == IO.MatchType.io_type: | |
| return True | |
| # This accounts for some custom nodes that output lists of options as the type; | |
| # if we ever want to break them on purpose, this can be removed | |
| if isinstance(received_type, list) and input_type == IO.Combo.io_type: | |
| return True | |
| # Not equal, and not strings | |
| if not isinstance(received_type, str) or not isinstance(input_type, str): | |
| return False | |
| # Split the type strings into sets for comparison | |
| received_types = set(t.strip() for t in received_type.split(",")) | |
| input_types = set(t.strip() for t in input_type.split(",")) | |
| # If any of the types is '*', we can return True immediately; this is the 'Any' type. | |
| if IO.AnyType.io_type in received_types or IO.AnyType.io_type in input_types: | |
| return True | |
| if strict: | |
| # In strict mode, all received types must be in the input types | |
| return received_types.issubset(input_types) | |
| else: | |
| # In non-strict mode, there must be at least one type in common | |
| return len(received_types.intersection(input_types)) > 0 | |