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"; | |
| import { ComfyWidgets } from "../../../scripts/widgets.js"; | |
| // Displays input text on a node | |
| // TODO: This should need to be so complicated. Refactor at some point. | |
| app.registerExtension({ | |
| name: "pysssss.ShowText", | |
| async beforeRegisterNodeDef(nodeType, nodeData, app) { | |
| if (nodeData.name === "ShowText|pysssss") { | |
| function populate(text) { | |
| if (this.widgets) { | |
| // On older frontend versions there is a hidden converted-widget | |
| const isConvertedWidget = +!!this.inputs?.[0].widget; | |
| for (let i = isConvertedWidget; i < this.widgets.length; i++) { | |
| this.widgets[i].onRemove?.(); | |
| } | |
| this.widgets.length = isConvertedWidget; | |
| } | |
| const v = [...text]; | |
| if (!v[0]) { | |
| v.shift(); | |
| } | |
| for (let list of v) { | |
| // Force list to be an array, not sure why sometimes it is/isn't | |
| if (!(list instanceof Array)) list = [list]; | |
| for (const l of list) { | |
| const w = ComfyWidgets["STRING"](this, "text_" + this.widgets?.length ?? 0, ["STRING", { multiline: true }], app).widget; | |
| w.inputEl.readOnly = true; | |
| w.inputEl.style.opacity = 0.6; | |
| w.value = l; | |
| } | |
| } | |
| requestAnimationFrame(() => { | |
| const sz = this.computeSize(); | |
| if (sz[0] < this.size[0]) { | |
| sz[0] = this.size[0]; | |
| } | |
| if (sz[1] < this.size[1]) { | |
| sz[1] = this.size[1]; | |
| } | |
| this.onResize?.(sz); | |
| app.graph.setDirtyCanvas(true, false); | |
| }); | |
| } | |
| // When the node is executed we will be sent the input text, display this in the widget | |
| const onExecuted = nodeType.prototype.onExecuted; | |
| nodeType.prototype.onExecuted = function (message) { | |
| onExecuted?.apply(this, arguments); | |
| populate.call(this, message.text); | |
| }; | |
| const VALUES = Symbol(); | |
| const configure = nodeType.prototype.configure; | |
| nodeType.prototype.configure = function () { | |
| // Store unmodified widget values as they get removed on configure by new frontend | |
| this[VALUES] = arguments[0]?.widgets_values; | |
| return configure?.apply(this, arguments); | |
| }; | |
| const onConfigure = nodeType.prototype.onConfigure; | |
| nodeType.prototype.onConfigure = function () { | |
| onConfigure?.apply(this, arguments); | |
| const widgets_values = this[VALUES]; | |
| if (widgets_values?.length) { | |
| // In newer frontend there seems to be a delay in creating the initial widget | |
| requestAnimationFrame(() => { | |
| populate.call(this, widgets_values.slice(+(widgets_values.length > 1 && this.inputs?.[0].widget))); | |
| }); | |
| } | |
| }; | |
| } | |
| }, | |
| }); | |