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
| const { app } = window.comfyAPI.app; | |
| function snapshotOld(oldNode) { | |
| const data = oldNode.serialize(); | |
| const widgetValuesByName = {}; | |
| for (const w of oldNode.widgets || []) widgetValuesByName[w.name] = w.value; | |
| const inputSnapshot = (oldNode.inputs || []).map(i => ({ | |
| name: i.name, | |
| widgetName: i.widget?.name ?? null, | |
| linkId: i.link ?? null, | |
| })); | |
| const outputLinksByName = {}; | |
| for (const o of oldNode.outputs || []) { | |
| if (o.links?.length) outputLinksByName[o.name] = [...o.links]; | |
| } | |
| return { data, widgetValuesByName, inputSnapshot, outputLinksByName }; | |
| } | |
| function applyCosmetics(newNode, old) { | |
| if (old.pos) newNode.pos = [...old.pos]; | |
| if (old.size) { | |
| const min = newNode.computeSize?.() || [0, 0]; | |
| newNode.size = [Math.max(old.size[0], min[0]), Math.max(old.size[1], min[1])]; | |
| } | |
| if (old.title) newNode.title = old.title; | |
| if (old.color) newNode.color = old.color; | |
| if (old.bgcolor) newNode.bgcolor = old.bgcolor; | |
| if (old.flags) newNode.flags = { ...(newNode.flags || {}), ...old.flags }; | |
| if (typeof old.mode === "number") newNode.mode = old.mode; | |
| if (old.properties) newNode.properties = { ...(newNode.properties || {}), ...old.properties }; | |
| } | |
| function fixNode(oldNode, comfyClass, { resetValues = false } = {}) { | |
| const graph = app.canvas.graph; | |
| const snap = snapshotOld(oldNode); | |
| const newNode = LiteGraph.createNode(comfyClass); | |
| if (!newNode) { | |
| console.error(`[KJNodes.FixNode] Unknown node type: ${comfyClass}`); | |
| return null; | |
| } | |
| try { | |
| graph.add(newNode, false); | |
| applyCosmetics(newNode, snap.data); | |
| // Restore widget values by name (live widgets, not widgets_values array). | |
| if (!resetValues) { | |
| for (const w of newNode.widgets || []) { | |
| if (Object.prototype.hasOwnProperty.call(snap.widgetValuesByName, w.name)) { | |
| try { w.value = snap.widgetValuesByName[w.name]; } catch {} | |
| } | |
| } | |
| } | |
| // Reconnect inputs by name. | |
| for (const inp of snap.inputSnapshot) { | |
| if (inp.linkId == null) continue; | |
| const link = app.graph.links[inp.linkId]; | |
| if (!link) continue; | |
| const src = app.graph.getNodeById(link.origin_id); | |
| const newSlot = newNode.findInputSlot?.(inp.name); | |
| if (!src || newSlot == null || newSlot < 0) continue; | |
| try { src.connect(link.origin_slot, newNode, newSlot); } catch {} | |
| } | |
| // Reconnect outputs by name. | |
| (newNode.outputs || []).forEach((out, i) => { | |
| const linkIds = snap.outputLinksByName[out.name]; | |
| if (!linkIds) return; | |
| for (const id of linkIds) { | |
| const link = app.graph.links[id]; | |
| if (!link) continue; | |
| const tgt = app.graph.getNodeById(link.target_id); | |
| if (!tgt) continue; | |
| try { newNode.connect(i, tgt, link.target_slot); } catch {} | |
| } | |
| }); | |
| graph.remove(oldNode); | |
| app.graph.afterChange(); | |
| requestAnimationFrame(() => app.canvas.setDirty(true, true)); | |
| return newNode; | |
| } catch (err) { | |
| console.error("[KJNodes.FixNode] Aborting, rolling back:", err); | |
| try { graph.remove(newNode); } catch {} | |
| return null; | |
| } | |
| } | |
| window.kjNodes = window.kjNodes || {}; | |
| window.kjNodes.recreateNode = fixNode; | |