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
| """Simplified tests for WebSocket feature flags functionality.""" | |
| from comfy_api import feature_flags | |
| class TestWebSocketFeatureFlags: | |
| """Test suite for WebSocket feature flags integration.""" | |
| def test_server_feature_flags_response(self): | |
| """Test server feature flags are properly formatted.""" | |
| features = feature_flags.get_server_features() | |
| # Check expected server features | |
| assert "supports_preview_metadata" in features | |
| assert features["supports_preview_metadata"] is True | |
| assert "max_upload_size" in features | |
| assert isinstance(features["max_upload_size"], (int, float)) | |
| def test_progress_py_checks_feature_flags(self): | |
| """Test that progress.py checks feature flags before sending metadata.""" | |
| # This simulates the check in progress.py | |
| client_id = "test_client" | |
| sockets_metadata = {"test_client": {"feature_flags": {}}} | |
| # The actual check would be in progress.py | |
| supports_metadata = feature_flags.supports_feature( | |
| sockets_metadata, client_id, "supports_preview_metadata" | |
| ) | |
| assert supports_metadata is False | |
| def test_multiple_clients_different_features(self): | |
| """Test handling multiple clients with different feature support.""" | |
| sockets_metadata = { | |
| "modern_client": { | |
| "feature_flags": {"supports_preview_metadata": True} | |
| }, | |
| "legacy_client": { | |
| "feature_flags": {} | |
| } | |
| } | |
| # Check modern client | |
| assert feature_flags.supports_feature( | |
| sockets_metadata, "modern_client", "supports_preview_metadata" | |
| ) is True | |
| # Check legacy client | |
| assert feature_flags.supports_feature( | |
| sockets_metadata, "legacy_client", "supports_preview_metadata" | |
| ) is False | |
| def test_feature_negotiation_message_format(self): | |
| """Test the format of feature negotiation messages.""" | |
| # Client message format | |
| client_message = { | |
| "type": "feature_flags", | |
| "data": { | |
| "supports_preview_metadata": True, | |
| "api_version": "1.0.0" | |
| } | |
| } | |
| # Verify structure | |
| assert client_message["type"] == "feature_flags" | |
| assert "supports_preview_metadata" in client_message["data"] | |
| # Server response format (what would be sent) | |
| server_features = feature_flags.get_server_features() | |
| server_message = { | |
| "type": "feature_flags", | |
| "data": server_features | |
| } | |
| # Verify structure | |
| assert server_message["type"] == "feature_flags" | |
| assert "supports_preview_metadata" in server_message["data"] | |
| assert server_message["data"]["supports_preview_metadata"] is True | |