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
| import os | |
| import threading | |
| class AnyType(str): | |
| def __ne__(self, __value: object) -> bool: | |
| return False | |
| any_typ = AnyType("*") | |
| def _make_target_folder_list(): | |
| """ | |
| Returns a list with 'custom' as the first option, | |
| plus all subfolders from get_model_subfolders. | |
| """ | |
| from .file_manager import get_model_subfolders | |
| subfolders = get_model_subfolders() | |
| return ["custom"] + subfolders | |
| class HuggingFaceDownloadModel: | |
| CATEGORY = "Hugging Face Downloaders 🤗" | |
| OUTPUT_NODE = True | |
| def INPUT_TYPES(cls): | |
| return { | |
| "required": { | |
| "target_folder": (_make_target_folder_list(), {"default": "loras"}), | |
| "link": ("STRING", {"default": ""}), | |
| }, | |
| "optional": { | |
| "custom_path": ("STRING", { | |
| "default": "", | |
| "visible_if": {"target_folder": "custom"} | |
| }), | |
| # "download_in_background": ("BOOLEAN", {"default": False, "label": "Download in background"}), | |
| } | |
| } | |
| def update_link_field(new_value, old_value): | |
| """ | |
| Update the link field to show the parsed view. | |
| """ | |
| from .parse_link import parse_link | |
| try: | |
| parsed = parse_link(new_value) | |
| repo = parsed.get("repo", "") | |
| subfolder = parsed.get("subfolder", "").strip("/") | |
| file = parsed.get("file", "").strip("/") | |
| updated_value = "/".join(filter(None, [repo, subfolder, file])) | |
| return updated_value | |
| except Exception as e: | |
| print(f"[ERROR] Failed to parse link: {e}") | |
| return new_value | |
| RETURN_TYPES = (any_typ,) | |
| RETURN_NAMES = ("model name",) | |
| FUNCTION = "download_model" | |
| def download_model(self, target_folder, link, custom_path="", download_in_background=False): | |
| """ | |
| 1) If user picks 'custom' in the combo, we interpret custom_path as final_folder, else just target_folder. | |
| 2) parse link => subfolder/file for single file | |
| 3) call run_download(...) which uses hf_hub_download so hf_xet can be used | |
| 4) node's return: | |
| - if target_folder != 'custom', we do just the filename | |
| - if 'custom', remove the first segment of custom_path (if any), then leftover + "/" + filename | |
| """ | |
| from .parse_link import parse_link | |
| from .downloader import run_download | |
| # Step 1: final_folder logic | |
| if target_folder == "custom": | |
| final_folder = custom_path.strip().rstrip("/\\") | |
| else: | |
| final_folder = target_folder.strip().rstrip("/\\") | |
| # Step 2: parse link | |
| try: | |
| parsed = parse_link(link) | |
| except Exception as e: | |
| return (f"Error parsing link: {e}",) | |
| # Step 3: run in background or sync | |
| if download_in_background: | |
| threading.Thread( | |
| target=run_download, | |
| args=(parsed, final_folder), | |
| daemon=True | |
| ).start() | |
| # best guess: use parsed["file"] | |
| if "file" in parsed: | |
| guessed_file = parsed["file"].strip("/") | |
| # if user used 'custom', do leftover logic | |
| if target_folder == "custom": | |
| segments = custom_path.strip("/\\").split("/") | |
| if len(segments) > 1: | |
| leftover = "/".join(segments[1:]).strip("/") | |
| if leftover: | |
| return (leftover + "/" + os.path.basename(guessed_file),) | |
| else: | |
| return (os.path.basename(guessed_file),) | |
| else: | |
| return (os.path.basename(guessed_file),) | |
| else: | |
| return (os.path.basename(guessed_file),) | |
| else: | |
| return ("",) # no file known | |
| else: | |
| # sync => we get final_message and local_path | |
| final_message, local_path = run_download(parsed, final_folder, sync=True) | |
| if local_path: | |
| # user wants leftover + "/" + filename if custom | |
| filename = os.path.basename(local_path) | |
| if target_folder == "custom": | |
| segments = custom_path.strip("/\\").split("/") | |
| if len(segments) > 1: | |
| leftover = "/".join(segments[1:]).strip("/") | |
| if leftover: | |
| return (leftover + "/" + filename,) | |
| else: | |
| return (filename,) | |
| else: | |
| return (filename,) | |
| else: | |
| return (filename,) | |
| else: | |
| return ("",) | |