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 type { | |
| IContextMenuValue, | |
| LGraphCanvas as TLGraphCanvas, | |
| LGraphNode, | |
| } from "@comfyorg/frontend"; | |
| import type {ComfyNodeDef} from "typings/comfy.js"; | |
| import {app} from "scripts/app.js"; | |
| import {rgthree} from "./rgthree.js"; | |
| import {getGroupNodes, getOutputNodes} from "./utils.js"; | |
| import {SERVICE as CONFIG_SERVICE} from "./services/config_service.js"; | |
| function showQueueNodesMenuIfOutputNodesAreSelected( | |
| existingOptions: (IContextMenuValue<unknown> | null)[], | |
| ) { | |
| if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) { | |
| return; | |
| } | |
| const outputNodes = getOutputNodes(Object.values(app.canvas.selected_nodes)); | |
| const menuItem = { | |
| content: `Queue Selected Output Nodes (rgthree) `, | |
| className: "rgthree-contextmenu-item", | |
| callback: () => { | |
| rgthree.queueOutputNodes(outputNodes); | |
| }, | |
| disabled: !outputNodes.length, | |
| }; | |
| let idx = existingOptions.findIndex((o) => o?.content === "Outputs") + 1; | |
| idx = idx || existingOptions.findIndex((o) => o?.content === "Align") + 1; | |
| idx = idx || 3; | |
| existingOptions.splice(idx, 0, menuItem); | |
| } | |
| function showQueueGroupNodesMenuIfGroupIsSelected( | |
| existingOptions: (IContextMenuValue<unknown> | null)[], | |
| ) { | |
| if (CONFIG_SERVICE.getConfigValue("features.menu_queue_selected_nodes") === false) { | |
| return; | |
| } | |
| const group = | |
| rgthree.lastCanvasMouseEvent && | |
| (app.canvas.getCurrentGraph() || app.graph).getGroupOnPos( | |
| rgthree.lastCanvasMouseEvent.canvasX, | |
| rgthree.lastCanvasMouseEvent.canvasY, | |
| ); | |
| const outputNodes: LGraphNode[] | null = (group && getOutputNodes(getGroupNodes(group))) || null; | |
| const menuItem = { | |
| content: `Queue Group Output Nodes (rgthree) `, | |
| className: "rgthree-contextmenu-item", | |
| callback: () => { | |
| outputNodes && rgthree.queueOutputNodes(outputNodes); | |
| }, | |
| disabled: !outputNodes?.length, | |
| }; | |
| let idx = existingOptions.findIndex((o) => o?.content?.startsWith("Queue Selected ")) + 1; | |
| idx = idx || existingOptions.findIndex((o) => o?.content === "Outputs") + 1; | |
| idx = idx || existingOptions.findIndex((o) => o?.content === "Align") + 1; | |
| idx = idx || 3; | |
| existingOptions.splice(idx, 0, menuItem); | |
| } | |
| /** | |
| * Adds a "Queue Node" menu item to all output nodes, working with `rgthree.queueOutputNode` to | |
| * execute only a single node's path. | |
| */ | |
| app.registerExtension({ | |
| name: "rgthree.QueueNode", | |
| async beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyNodeDef) { | |
| const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; | |
| nodeType.prototype.getExtraMenuOptions = function ( | |
| canvas: TLGraphCanvas, | |
| options: (IContextMenuValue<unknown> | null)[], | |
| ): (IContextMenuValue<unknown> | null)[] { | |
| const extraOptions = getExtraMenuOptions?.call(this, canvas, options) ?? []; | |
| showQueueNodesMenuIfOutputNodesAreSelected(options); | |
| showQueueGroupNodesMenuIfGroupIsSelected(options); | |
| return extraOptions; | |
| }; | |
| }, | |
| async setup() { | |
| const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions; | |
| LGraphCanvas.prototype.getCanvasMenuOptions = function (...args: any[]) { | |
| const options = getCanvasMenuOptions.apply(this, [...args] as any); | |
| showQueueNodesMenuIfOutputNodesAreSelected(options); | |
| showQueueGroupNodesMenuIfGroupIsSelected(options); | |
| return options; | |
| }; | |
| }, | |
| }); | |