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
| from urllib.parse import urlparse, parse_qs, unquote | |
| def parse_link(link: str) -> dict: | |
| """ | |
| Parse a Hugging Face URL or shorthand string. | |
| Supports URLs with keywords "resolve", "blob", or "tree". | |
| Returns a dictionary with keys: | |
| - repo: e.g., "username/repo" | |
| - revision: if present (e.g., "main") | |
| - subfolder: if present (the path inside the repo) | |
| - file: if present (the file name for file downloads) | |
| """ | |
| parsed_url = urlparse(link) | |
| if parsed_url.scheme: | |
| path_parts = [unquote(p) for p in parsed_url.path.strip("/").split("/")] | |
| else: | |
| path_parts = [unquote(p) for p in link.strip("/").split("/")] | |
| result = {} | |
| if len(path_parts) >= 2: | |
| result["repo"] = f"{path_parts[0]}/{path_parts[1]}" | |
| else: | |
| raise ValueError("Link does not contain repository information.") | |
| if "resolve" in path_parts: | |
| idx = path_parts.index("resolve") | |
| if len(path_parts) > idx + 1: | |
| result["revision"] = path_parts[idx+1] | |
| if len(path_parts) > idx + 2: | |
| remaining = path_parts[idx+2:] | |
| if remaining: | |
| if len(remaining) > 1: | |
| result["subfolder"] = "/".join(remaining[:-1]) | |
| result["file"] = remaining[-1] | |
| elif "blob" in path_parts: | |
| idx = path_parts.index("blob") | |
| if len(path_parts) > idx + 1: | |
| result["revision"] = path_parts[idx+1] | |
| if len(path_parts) > idx + 2: | |
| remaining = path_parts[idx+2:] | |
| if remaining: | |
| if len(remaining) > 1: | |
| result["subfolder"] = "/".join(remaining[:-1]) | |
| result["file"] = remaining[-1] | |
| elif "tree" in path_parts: | |
| idx = path_parts.index("tree") | |
| if len(path_parts) > idx + 1: | |
| result["revision"] = path_parts[idx+1] | |
| if len(path_parts) > idx + 2: | |
| result["subfolder"] = "/".join(path_parts[idx+2:]) | |
| else: | |
| if len(path_parts) > 2: | |
| if "." in path_parts[-1]: | |
| result["subfolder"] = "/".join(path_parts[2:-1]) | |
| result["file"] = path_parts[-1] | |
| else: | |
| result["subfolder"] = "/".join(path_parts[2:]) | |
| return result | |