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 {ISerialisedGraph} from "@comfyorg/frontend"; | |
| import type {ComfyApiFormat} from "typings/comfy.js"; | |
| import {getResolver} from "./shared_utils.js"; | |
| import {getPngMetadata, getWebpMetadata} from "./comfyui_shim.js"; | |
| /** | |
| * Parses the workflow JSON and do any necessary cleanup. | |
| */ | |
| function parseWorkflowJson(stringJson?: string) { | |
| stringJson = stringJson || "null"; | |
| // Starting around August 2024 the serialized JSON started to get messy and contained `NaN` (for | |
| // an is_changed property, specifically). NaN is not parseable, so we'll get those on out of there | |
| // and cleanup anything else we need. | |
| stringJson = stringJson.replace(/:\s*NaN/g, ": null"); | |
| return JSON.parse(stringJson); | |
| } | |
| export async function tryToGetWorkflowDataFromEvent( | |
| e: DragEvent, | |
| ): Promise<{workflow: ISerialisedGraph | null; prompt: ComfyApiFormat | null}> { | |
| let work; | |
| for (const file of e.dataTransfer?.files || []) { | |
| const data = await tryToGetWorkflowDataFromFile(file); | |
| if (data.workflow || data.prompt) { | |
| return data; | |
| } | |
| } | |
| const validTypes = ["text/uri-list", "text/x-moz-url"]; | |
| const match = (e.dataTransfer?.types || []).find((t) => validTypes.find((v) => t === v)); | |
| if (match) { | |
| const uri = e.dataTransfer!.getData(match)?.split("\n")?.[0]; | |
| if (uri) { | |
| return tryToGetWorkflowDataFromFile(await (await fetch(uri)).blob()); | |
| } | |
| } | |
| return {workflow: null, prompt: null}; | |
| } | |
| export async function tryToGetWorkflowDataFromFile( | |
| file: File | Blob, | |
| ): Promise<{workflow: ISerialisedGraph | null; prompt: ComfyApiFormat | null}> { | |
| if (file.type === "image/png") { | |
| const pngInfo = await getPngMetadata(file); | |
| return { | |
| workflow: parseWorkflowJson(pngInfo?.workflow), | |
| prompt: parseWorkflowJson(pngInfo?.prompt), | |
| }; | |
| } | |
| if (file.type === "image/webp") { | |
| const pngInfo = await getWebpMetadata(file); | |
| // Support loading workflows from that webp custom node. | |
| const workflow = parseWorkflowJson(pngInfo?.workflow || pngInfo?.Workflow || "null"); | |
| const prompt = parseWorkflowJson(pngInfo?.prompt || pngInfo?.Prompt || "null"); | |
| return {workflow, prompt}; | |
| } | |
| if (file.type === "application/json" || (file as File).name?.endsWith(".json")) { | |
| const resolver = getResolver<{workflow: any; prompt: any}>(); | |
| const reader = new FileReader(); | |
| reader.onload = async () => { | |
| const json = parseWorkflowJson(reader.result as string); | |
| const isApiJson = Object.values(json).every((v: any) => v.class_type); | |
| const prompt = isApiJson ? json : null; | |
| const workflow = !isApiJson && !json?.templates ? json : null; | |
| return {workflow, prompt}; | |
| }; | |
| return resolver.promise; | |
| } | |
| return {workflow: null, prompt: null}; | |
| } | |