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
| """ | |
| Unit tests for Groq model fetching cache behavior. | |
| Run: python test_groq_cache.py | |
| Note: These tests verify the caching and fallback logic. | |
| Full integration requires ComfyUI environment. | |
| """ | |
| import sys | |
| import os | |
| # Mock the comfy_api import for standalone testing | |
| class MockIO: | |
| class Input: | |
| def __init__(self, name, **kwargs): | |
| self.name = name | |
| self.__dict__.update(kwargs) | |
| class Schema: | |
| def __init__(self, **kwargs): | |
| self.__dict__.update(kwargs) | |
| class NodeOutput: | |
| def __init__(self, response, status, help_text=None): | |
| self.response = response | |
| self.status = status | |
| self.help_text = help_text | |
| class ComfyNode: | |
| pass | |
| class MockComfyExtension: | |
| pass | |
| sys.modules['comfy_api'] = type(sys)('comfy_api') | |
| sys.modules['comfy_api.latest'] = type(sys)('comfy_api.latest') | |
| sys.modules['comfy_api.latest'].ComfyExtension = MockComfyExtension | |
| sys.modules['comfy_api.latest'].io = MockIO | |
| # Now import the module | |
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) | |
| import groq_node | |
| def test_cache_initial_state(): | |
| """Cache starts empty.""" | |
| assert groq_node._groq_model_cache["models"] is None | |
| assert groq_node._groq_model_cache["vision_models"] is None | |
| print("[PASS] Cache initial state test passed") | |
| def test_static_fallback_without_api_key(): | |
| """Returns static fallback when no API key provided.""" | |
| models, vision_models = groq_node._fetch_groq_models(api_key=None) | |
| assert models is not None | |
| assert len(models) > 0 | |
| assert "Manual Input" in models | |
| assert any("--- Featured ---" in m for m in models) | |
| print(f"[PASS] Static fallback test passed ({len(models)} models)") | |
| def test_static_fallback_function(): | |
| """_get_static_fallback_models returns proper structure.""" | |
| models, vision = groq_node._get_static_fallback_models() | |
| assert isinstance(models, list) | |
| assert isinstance(vision, list) | |
| assert len(models) > 0 | |
| assert len(vision) > 0 | |
| assert "Manual Input" in models | |
| print(f"[PASS] Static fallback function test passed ({len(models)} models, {len(vision)} vision)") | |
| def test_categorization_structure(): | |
| """Model list has category headers.""" | |
| models, _ = groq_node._fetch_groq_models(api_key=None) | |
| categories_found = [m for m in models if m.startswith("---")] | |
| assert len(categories_found) >= 5 | |
| # Manual Input section exists | |
| assert "Manual Input" in models | |
| print(f"[PASS] Categorization structure test passed ({len(categories_found)} categories)") | |
| def test_model_categories_mapping(): | |
| """MODEL_CATEGORIES has expected structure.""" | |
| assert isinstance(groq_node.MODEL_CATEGORIES, dict) | |
| assert "Featured" in groq_node.MODEL_CATEGORIES | |
| assert "Production: Chat" in groq_node.MODEL_CATEGORIES | |
| assert len(groq_node.MODEL_CATEGORIES) >= 7 | |
| print(f"[PASS] Model categories mapping test passed ({len(groq_node.MODEL_CATEGORIES)} categories)") | |
| def test_vision_patterns(): | |
| """VISION_PATTERNS has expected patterns.""" | |
| assert isinstance(groq_node.VISION_PATTERNS, list) | |
| assert len(groq_node.VISION_PATTERNS) > 0 | |
| assert "vision" in groq_node.VISION_PATTERNS | |
| assert "vl" in groq_node.VISION_PATTERNS | |
| print(f"[PASS] Vision patterns test passed ({len(groq_node.VISION_PATTERNS)} patterns)") | |
| def test_known_vision_models(): | |
| """KNOWN_VISION_MODELS has expected models.""" | |
| assert isinstance(groq_node.KNOWN_VISION_MODELS, list) | |
| assert len(groq_node.KNOWN_VISION_MODELS) > 0 | |
| assert "meta-llama/llama-4-scout-17b-16e-instruct" in groq_node.KNOWN_VISION_MODELS | |
| print(f"[PASS] Known vision models test passed ({len(groq_node.KNOWN_VISION_MODELS)} models)") | |
| if __name__ == "__main__": | |
| print("Running Groq Cache Tests...\n") | |
| test_cache_initial_state() | |
| test_static_fallback_without_api_key() | |
| test_static_fallback_function() | |
| test_categorization_structure() | |
| test_model_categories_mapping() | |
| test_vision_patterns() | |
| test_known_vision_models() | |
| print("\n[SUCCESS] All cache tests passed!") | |