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
| class RgthreeApi { | |
| constructor(baseUrl) { | |
| this.getCheckpointsPromise = null; | |
| this.getSamplersPromise = null; | |
| this.getSchedulersPromise = null; | |
| this.getLorasPromise = null; | |
| this.getWorkflowsPromise = null; | |
| this.setBaseUrl(baseUrl); | |
| } | |
| setBaseUrl(baseUrlArg) { | |
| var _a; | |
| let baseUrl = null; | |
| if (baseUrlArg) { | |
| baseUrl = baseUrlArg; | |
| } | |
| else if (window.location.pathname.includes("/rgthree/")) { | |
| const parts = (_a = window.location.pathname.split("/rgthree/")[1]) === null || _a === void 0 ? void 0 : _a.split("/"); | |
| if (parts && parts.length) { | |
| baseUrl = parts.map(() => "../").join("") + "rgthree/api"; | |
| } | |
| } | |
| this.baseUrl = baseUrl || "./rgthree/api"; | |
| const comfyBasePathname = location.pathname.includes("/rgthree/") | |
| ? location.pathname.split("rgthree/")[0] | |
| : location.pathname; | |
| this.comfyBaseUrl = comfyBasePathname.split("/").slice(0, -1).join("/"); | |
| } | |
| apiURL(route) { | |
| return `${this.baseUrl}${route}`; | |
| } | |
| fetchApi(route, options) { | |
| return fetch(this.apiURL(route), options); | |
| } | |
| async fetchJson(route, options) { | |
| const r = await this.fetchApi(route, options); | |
| return await r.json(); | |
| } | |
| async postJson(route, json) { | |
| const body = new FormData(); | |
| body.append("json", JSON.stringify(json)); | |
| return await rgthreeApi.fetchJson(route, { method: "POST", body }); | |
| } | |
| getLoras(force = false) { | |
| if (!this.getLorasPromise || force) { | |
| this.getLorasPromise = this.fetchJson("/loras?format=details", { cache: "no-store" }); | |
| } | |
| return this.getLorasPromise; | |
| } | |
| async fetchApiJsonOrNull(route, options) { | |
| const response = await this.fetchJson(route, options); | |
| if (response.status === 200 && response.data) { | |
| return response.data || null; | |
| } | |
| return null; | |
| } | |
| async getModelsInfo(options) { | |
| var _a; | |
| const params = new URLSearchParams(); | |
| if ((_a = options.files) === null || _a === void 0 ? void 0 : _a.length) { | |
| params.set("files", options.files.join(",")); | |
| } | |
| if (options.light) { | |
| params.set("light", "1"); | |
| } | |
| if (options.format) { | |
| params.set("format", options.format); | |
| } | |
| const path = `/${options.type}/info?` + params.toString(); | |
| return (await this.fetchApiJsonOrNull(path)) || []; | |
| } | |
| async getLorasInfo(options = {}) { | |
| return this.getModelsInfo({ type: "loras", ...options }); | |
| } | |
| async getCheckpointsInfo(options = {}) { | |
| return this.getModelsInfo({ type: "checkpoints", ...options }); | |
| } | |
| async refreshModelsInfo(options) { | |
| var _a; | |
| const params = new URLSearchParams(); | |
| if ((_a = options.files) === null || _a === void 0 ? void 0 : _a.length) { | |
| params.set("files", options.files.join(",")); | |
| } | |
| const path = `/${options.type}/info/refresh?` + params.toString(); | |
| const infos = await this.fetchApiJsonOrNull(path); | |
| return infos; | |
| } | |
| async refreshLorasInfo(options = {}) { | |
| return this.refreshModelsInfo({ type: "loras", ...options }); | |
| } | |
| async refreshCheckpointsInfo(options = {}) { | |
| return this.refreshModelsInfo({ type: "checkpoints", ...options }); | |
| } | |
| async clearModelsInfo(options) { | |
| var _a; | |
| const params = new URLSearchParams(); | |
| if ((_a = options.files) === null || _a === void 0 ? void 0 : _a.length) { | |
| params.set("files", options.files.join(",")); | |
| } | |
| const path = `/${options.type}/info/clear?` + params.toString(); | |
| await this.fetchApiJsonOrNull(path); | |
| return; | |
| } | |
| async clearLorasInfo(options = {}) { | |
| return this.clearModelsInfo({ type: "loras", ...options }); | |
| } | |
| async clearCheckpointsInfo(options = {}) { | |
| return this.clearModelsInfo({ type: "checkpoints", ...options }); | |
| } | |
| async saveModelInfo(type, file, data) { | |
| const body = new FormData(); | |
| body.append("json", JSON.stringify(data)); | |
| return await this.fetchApiJsonOrNull(`/${type}/info?file=${encodeURIComponent(file)}`, { cache: "no-store", method: "POST", body }); | |
| } | |
| async saveLoraInfo(file, data) { | |
| return this.saveModelInfo("loras", file, data); | |
| } | |
| async saveCheckpointsInfo(file, data) { | |
| return this.saveModelInfo("checkpoints", file, data); | |
| } | |
| fetchComfyApi(route, options) { | |
| const url = this.comfyBaseUrl + "/api" + route; | |
| options = options || {}; | |
| options.headers = options.headers || {}; | |
| options.cache = options.cache || "no-cache"; | |
| return fetch(url, options); | |
| } | |
| print(messageType) { | |
| this.fetchApi(`/print?type=${messageType}`, {}); | |
| } | |
| } | |
| export const rgthreeApi = new RgthreeApi(); | |