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 glob | |
| import os | |
| from nodes import LoraLoader, CheckpointLoaderSimple | |
| import folder_paths | |
| from server import PromptServer | |
| from folder_paths import get_directory_by_type | |
| from aiohttp import web | |
| import shutil | |
| async def view(request): | |
| name = request.match_info["name"] | |
| pos = name.index("/") | |
| type = name[0:pos] | |
| name = name[pos+1:] | |
| image_path = folder_paths.get_full_path( | |
| type, name) | |
| if not image_path: | |
| return web.Response(status=404) | |
| filename = os.path.basename(image_path) | |
| return web.FileResponse(image_path, headers={"Content-Disposition": f"filename=\"{filename}\""}) | |
| async def save_preview(request): | |
| name = request.match_info["name"] | |
| pos = name.index("/") | |
| type = name[0:pos] | |
| name = name[pos+1:] | |
| body = await request.json() | |
| dir = get_directory_by_type(body.get("type", "output")) | |
| subfolder = body.get("subfolder", "") | |
| full_output_folder = os.path.join(dir, os.path.normpath(subfolder)) | |
| filepath = os.path.join(full_output_folder, body.get("filename", "")) | |
| if os.path.commonpath((dir, os.path.abspath(filepath))) != dir: | |
| return web.Response(status=400) | |
| image_path = folder_paths.get_full_path(type, name) | |
| image_path = os.path.splitext( | |
| image_path)[0] + os.path.splitext(filepath)[1] | |
| shutil.copyfile(filepath, image_path) | |
| return web.json_response({ | |
| "image": type + "/" + os.path.basename(image_path) | |
| }) | |
| async def get_examples(request): | |
| name = request.match_info["name"] | |
| pos = name.index("/") | |
| type = name[0:pos] | |
| name = name[pos+1:] | |
| file_path = folder_paths.get_full_path( | |
| type, name) | |
| if not file_path: | |
| return web.Response(status=404) | |
| file_path_no_ext = os.path.splitext(file_path)[0] | |
| examples = [] | |
| if os.path.isdir(file_path_no_ext): | |
| examples += sorted(map(lambda t: os.path.relpath(t, file_path_no_ext), | |
| glob.glob(file_path_no_ext + "/*.txt"))) | |
| if os.path.isfile(file_path_no_ext + ".txt"): | |
| examples += ["notes"] | |
| return web.json_response(examples) | |
| async def save_example(request): | |
| name = request.match_info["name"] | |
| pos = name.index("/") | |
| type = name[0:pos] | |
| name = name[pos+1:] | |
| body = await request.json() | |
| example_name = body["name"] | |
| example = body["example"] | |
| file_path = folder_paths.get_full_path( | |
| type, name) | |
| if not file_path: | |
| return web.Response(status=404) | |
| if not example_name.endswith(".txt"): | |
| example_name += ".txt" | |
| file_path_no_ext = os.path.splitext(file_path)[0] | |
| example_file = os.path.join(file_path_no_ext, example_name) | |
| if not os.path.exists(file_path_no_ext): | |
| os.mkdir(file_path_no_ext) | |
| with open(example_file, 'w', encoding='utf8') as f: | |
| f.write(example) | |
| return web.Response(status=201) | |
| async def get_images(request): | |
| type = request.match_info["type"] | |
| names = folder_paths.get_filename_list(type) | |
| images = {} | |
| for item_name in names: | |
| file_name = os.path.splitext(item_name)[0] | |
| file_path = folder_paths.get_full_path(type, item_name) | |
| if file_path is None: | |
| continue | |
| file_path_no_ext = os.path.splitext(file_path)[0] | |
| for ext in ["png", "jpg", "jpeg", "preview.png", "preview.jpeg"]: | |
| if os.path.isfile(file_path_no_ext + "." + ext): | |
| images[item_name] = f"{type}/{file_name}.{ext}" | |
| break | |
| return web.json_response(images) | |
| class LoraLoaderWithImages(LoraLoader): | |
| RETURN_TYPES = (*LoraLoader.RETURN_TYPES, "STRING",) | |
| RETURN_NAMES = (*getattr(LoraLoader, "RETURN_NAMES", | |
| LoraLoader.RETURN_TYPES), "example") | |
| def INPUT_TYPES(s): | |
| types = super().INPUT_TYPES() | |
| types["optional"] = {"prompt": ("STRING", {"hidden": True})} | |
| return types | |
| def load_lora(self, **kwargs): | |
| prompt = kwargs.pop("prompt", "") | |
| return (*super().load_lora(**kwargs), prompt) | |
| class CheckpointLoaderSimpleWithImages(CheckpointLoaderSimple): | |
| RETURN_TYPES = (*CheckpointLoaderSimple.RETURN_TYPES, "STRING",) | |
| RETURN_NAMES = (*getattr(CheckpointLoaderSimple, "RETURN_NAMES", | |
| CheckpointLoaderSimple.RETURN_TYPES), "example") | |
| def INPUT_TYPES(s): | |
| types = super().INPUT_TYPES() | |
| types["optional"] = {"prompt": ("STRING", {"hidden": True})} | |
| return types | |
| def load_checkpoint(self, **kwargs): | |
| prompt = kwargs.pop("prompt", "") | |
| return (*super().load_checkpoint(**kwargs), prompt) | |
| NODE_CLASS_MAPPINGS = { | |
| "LoraLoader|pysssss": LoraLoaderWithImages, | |
| "CheckpointLoader|pysssss": CheckpointLoaderSimpleWithImages, | |
| } | |
| NODE_DISPLAY_NAME_MAPPINGS = { | |
| "LoraLoader|pysssss": "Lora Loader 🐍", | |
| "CheckpointLoader|pysssss": "Checkpoint Loader 🐍", | |
| } | |