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="models/text_encoders/Qwen3VL-8B-Uncensored-HauhauCS-Aggressive-Q8_0.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:Q8_0 # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saik0s/comfy_backup:Q8_0 # Run inference directly in the terminal: llama cli -hf saik0s/comfy_backup:Q8_0
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:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf saik0s/comfy_backup:Q8_0
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:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf saik0s/comfy_backup:Q8_0
Use Docker
docker model run hf.co/saik0s/comfy_backup:Q8_0
- LM Studio
- Jan
- Ollama
How to use saik0s/comfy_backup with Ollama:
ollama run hf.co/saik0s/comfy_backup:Q8_0
- 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:Q8_0
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:Q8_0" } ] } } }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:Q8_0
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:Q8_0
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:Q8_0
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:Q8_0" \ --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:Q8_0
- Lemonade
How to use saik0s/comfy_backup with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saik0s/comfy_backup:Q8_0
Run and chat with the model
lemonade run user.comfy_backup-Q8_0
List all available models
lemonade list
| import { app } from "../../scripts/app.js"; | |
| import { rgthreeApi } from "../../rgthree/common/rgthree_api.js"; | |
| const PASS_THROUGH = function (item) { | |
| return item; | |
| }; | |
| export async function showLoraChooser(event, callback, parentMenu, loras) { | |
| var _a, _b; | |
| const canvas = app.canvas; | |
| if (!loras) { | |
| loras = ["None", ...(await rgthreeApi.getLoras().then((loras) => loras.map((l) => l.file)))]; | |
| } | |
| new LiteGraph.ContextMenu(loras, { | |
| event: event, | |
| parentMenu: parentMenu != null ? parentMenu : undefined, | |
| title: "Choose a lora", | |
| scale: Math.max(1, (_b = (_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1), | |
| className: "dark", | |
| callback, | |
| }); | |
| } | |
| export function showNodesChooser(event, mapFn, callback, parentMenu) { | |
| var _a, _b; | |
| const canvas = app.canvas; | |
| const nodesOptions = app.graph._nodes | |
| .map(mapFn) | |
| .filter((e) => e != null); | |
| nodesOptions.sort((a, b) => { | |
| return a.value - b.value; | |
| }); | |
| new LiteGraph.ContextMenu(nodesOptions, { | |
| event: event, | |
| parentMenu, | |
| title: "Choose a node id", | |
| scale: Math.max(1, (_b = (_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1), | |
| className: "dark", | |
| callback, | |
| }); | |
| } | |
| export function showWidgetsChooser(event, node, mapFn, callback, parentMenu) { | |
| var _a, _b; | |
| const options = (node.widgets || []) | |
| .map(mapFn) | |
| .filter((e) => e != null); | |
| if (options.length) { | |
| const canvas = app.canvas; | |
| new LiteGraph.ContextMenu(options, { | |
| event, | |
| parentMenu, | |
| title: "Choose an input/widget", | |
| scale: Math.max(1, (_b = (_a = canvas.ds) === null || _a === void 0 ? void 0 : _a.scale) !== null && _b !== void 0 ? _b : 1), | |
| className: "dark", | |
| callback, | |
| }); | |
| } | |
| } | |