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 { app } from "../../../scripts/app.js"; | |
| app.registerExtension({ | |
| name: "pysssss.ContextMenuHook", | |
| init() { | |
| const getOrSet = (target, name, create) => { | |
| if (name in target) return target[name]; | |
| return (target[name] = create()); | |
| }; | |
| const symbol = getOrSet(window, "__pysssss__", () => Symbol("__pysssss__")); | |
| const store = getOrSet(window, symbol, () => ({})); | |
| const contextMenuHook = getOrSet(store, "contextMenuHook", () => ({})); | |
| for (const e of ["ctor", "preAddItem", "addItem"]) { | |
| if (!contextMenuHook[e]) { | |
| contextMenuHook[e] = []; | |
| } | |
| } | |
| // Big ol' hack to get allow customizing the context menu | |
| // Replace the addItem function with our own that wraps the context of "this" with a proxy | |
| // That proxy then replaces the constructor with another proxy | |
| // That proxy then calls the custom ContextMenu that supports filters | |
| const ctorProxy = new Proxy(LiteGraph.ContextMenu, { | |
| construct(target, args) { | |
| return new LiteGraph.ContextMenu(...args); | |
| }, | |
| }); | |
| function triggerCallbacks(name, getArgs, handler) { | |
| const callbacks = contextMenuHook[name]; | |
| if (callbacks && callbacks instanceof Array) { | |
| for (const cb of callbacks) { | |
| const r = cb(...getArgs()); | |
| handler?.call(this, r); | |
| } | |
| } else { | |
| console.warn("[pysssss 🐍]", `invalid ${name} callbacks`, callbacks, name in contextMenuHook); | |
| } | |
| } | |
| const addItem = LiteGraph.ContextMenu.prototype.addItem; | |
| LiteGraph.ContextMenu.prototype.addItem = function () { | |
| const proxy = new Proxy(this, { | |
| get(target, prop) { | |
| if (prop === "constructor") { | |
| return ctorProxy; | |
| } | |
| return target[prop]; | |
| }, | |
| }); | |
| proxy.__target__ = this; | |
| let el; | |
| let args = arguments; | |
| triggerCallbacks( | |
| "preAddItem", | |
| () => [el, this, args], | |
| (r) => { | |
| if (r !== undefined) el = r; | |
| } | |
| ); | |
| if (el === undefined) { | |
| el = addItem.apply(proxy, arguments); | |
| } | |
| triggerCallbacks( | |
| "addItem", | |
| () => [el, this, args], | |
| (r) => { | |
| if (r !== undefined) el = r; | |
| } | |
| ); | |
| return el; | |
| }; | |
| // We also need to patch the ContextMenu constructor to unwrap the parent else it fails a LiteGraph type check | |
| const ctxMenu = LiteGraph.ContextMenu; | |
| LiteGraph.ContextMenu = function (values, options) { | |
| if (options?.parentMenu) { | |
| if (options.parentMenu.__target__) { | |
| options.parentMenu = options.parentMenu.__target__; | |
| } | |
| } | |
| triggerCallbacks("ctor", () => [values, options]); | |
| return ctxMenu.call(this, values, options); | |
| }; | |
| LiteGraph.ContextMenu.prototype = ctxMenu.prototype; | |
| }, | |
| }); | |