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 pytest | |
| from comfy_execution.validation import validate_node_input | |
| def test_exact_match(): | |
| """Test cases where types match exactly""" | |
| assert validate_node_input("STRING", "STRING") | |
| assert validate_node_input("STRING,INT", "STRING,INT") | |
| assert validate_node_input("INT,STRING", "STRING,INT") # Order shouldn't matter | |
| def test_strict_mode(): | |
| """Test strict mode validation""" | |
| # Should pass - received type is subset of input type | |
| assert validate_node_input("STRING", "STRING,INT", strict=True) | |
| assert validate_node_input("INT", "STRING,INT", strict=True) | |
| assert validate_node_input("STRING,INT", "STRING,INT,BOOLEAN", strict=True) | |
| # Should fail - received type is not subset of input type | |
| assert not validate_node_input("STRING,INT", "STRING", strict=True) | |
| assert not validate_node_input("STRING,BOOLEAN", "STRING", strict=True) | |
| assert not validate_node_input("INT,BOOLEAN", "STRING,INT", strict=True) | |
| def test_non_strict_mode(): | |
| """Test non-strict mode validation (default behavior)""" | |
| # Should pass - types have overlap | |
| assert validate_node_input("STRING,BOOLEAN", "STRING,INT") | |
| assert validate_node_input("STRING,INT", "INT,BOOLEAN") | |
| assert validate_node_input("STRING", "STRING,INT") | |
| # Should fail - no overlap in types | |
| assert not validate_node_input("BOOLEAN", "STRING,INT") | |
| assert not validate_node_input("FLOAT", "STRING,INT") | |
| assert not validate_node_input("FLOAT,BOOLEAN", "STRING,INT") | |
| def test_whitespace_handling(): | |
| """Test that whitespace is handled correctly""" | |
| assert validate_node_input("STRING, INT", "STRING,INT") | |
| assert validate_node_input("STRING,INT", "STRING, INT") | |
| assert validate_node_input(" STRING , INT ", "STRING,INT") | |
| assert validate_node_input("STRING,INT", " STRING , INT ") | |
| def test_empty_strings(): | |
| """Test behavior with empty strings""" | |
| assert validate_node_input("", "") | |
| assert not validate_node_input("STRING", "") | |
| assert not validate_node_input("", "STRING") | |
| def test_single_vs_multiple(): | |
| """Test single type against multiple types""" | |
| assert validate_node_input("STRING", "STRING,INT,BOOLEAN") | |
| assert validate_node_input("STRING,INT,BOOLEAN", "STRING", strict=False) | |
| assert not validate_node_input("STRING,INT,BOOLEAN", "STRING", strict=True) | |
| def test_non_string(): | |
| """Test non-string types""" | |
| obj1 = object() | |
| obj2 = object() | |
| assert validate_node_input(obj1, obj1) | |
| assert not validate_node_input(obj1, obj2) | |
| class NotEqualsOverrideTest(str): | |
| """Test class for ``__ne__`` override.""" | |
| def __ne__(self, value: object) -> bool: | |
| if self == "*" or value == "*": | |
| return False | |
| if self == "LONGER_THAN_2": | |
| return not len(value) > 2 | |
| raise TypeError("This is a class for unit tests only.") | |
| def test_ne_override(): | |
| """Test ``__ne__`` any override""" | |
| any = NotEqualsOverrideTest("*") | |
| invalid_type = "INVALID_TYPE" | |
| obj = object() | |
| assert validate_node_input(any, any) | |
| assert validate_node_input(any, invalid_type) | |
| assert validate_node_input(any, obj) | |
| assert validate_node_input(any, {}) | |
| assert validate_node_input(any, []) | |
| assert validate_node_input(any, [1, 2, 3]) | |
| def test_ne_custom_override(): | |
| """Test ``__ne__`` custom override""" | |
| special = NotEqualsOverrideTest("LONGER_THAN_2") | |
| assert validate_node_input(special, special) | |
| assert validate_node_input(special, "*") | |
| assert validate_node_input(special, "INVALID_TYPE") | |
| assert validate_node_input(special, [1, 2, 3]) | |
| # Should fail | |
| assert not validate_node_input(special, [1, 2]) | |
| assert not validate_node_input(special, "TY") | |
| def test_parametrized_cases(received, input_type, strict, expected): | |
| """Parametrized test cases for various scenarios""" | |
| assert validate_node_input(received, input_type, strict) == expected | |