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"; | |
| app.registerExtension({ | |
| name: "pysssss.GraphArrange", | |
| setup(app) { | |
| const orig = LGraphCanvas.prototype.getCanvasMenuOptions; | |
| LGraphCanvas.prototype.getCanvasMenuOptions = function () { | |
| const options = orig.apply(this, arguments); | |
| options.push({ content: "Arrange (float left)", callback: () => graph.arrange() }); | |
| options.push({ | |
| content: "Arrange (float right)", | |
| callback: () => { | |
| (function () { | |
| var margin = 50; | |
| var layout; | |
| const nodes = this.computeExecutionOrder(false, true); | |
| const columns = []; | |
| // Find node first use | |
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| if (!outNode) continue; | |
| var l = outNode._level - 1; | |
| if (max === null) max = l; | |
| else if (l < max) max = l; | |
| } | |
| } | |
| } | |
| if (max != null) node._level = max; | |
| } | |
| for (let i = 0; i < nodes.length; ++i) { | |
| const node = nodes[i]; | |
| const col = node._level || 1; | |
| if (!columns[col]) { | |
| columns[col] = []; | |
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| columns[col].push(node); | |
| } | |
| let x = margin; | |
| for (let i = 0; i < columns.length; ++i) { | |
| const column = columns[i]; | |
| if (!column) { | |
| continue; | |
| } | |
| column.sort((a, b) => { | |
| var as = !(a.type === "SaveImage" || a.type === "PreviewImage"); | |
| var bs = !(b.type === "SaveImage" || b.type === "PreviewImage"); | |
| var r = as - bs; | |
| if (r === 0) r = (a.inputs?.length || 0) - (b.inputs?.length || 0); | |
| if (r === 0) r = (a.outputs?.length || 0) - (b.outputs?.length || 0); | |
| return r; | |
| }); | |
| let max_size = 100; | |
| let y = margin + LiteGraph.NODE_TITLE_HEIGHT; | |
| for (let j = 0; j < column.length; ++j) { | |
| const node = column[j]; | |
| node.pos[0] = layout == LiteGraph.VERTICAL_LAYOUT ? y : x; | |
| node.pos[1] = layout == LiteGraph.VERTICAL_LAYOUT ? x : y; | |
| const max_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 1 : 0; | |
| if (node.size[max_size_index] > max_size) { | |
| max_size = node.size[max_size_index]; | |
| } | |
| const node_size_index = layout == LiteGraph.VERTICAL_LAYOUT ? 0 : 1; | |
| y += node.size[node_size_index] + margin + LiteGraph.NODE_TITLE_HEIGHT + j; | |
| } | |
| // Right align in column | |
| for (let j = 0; j < column.length; ++j) { | |
| const node = column[j]; | |
| node.pos[0] += max_size - node.size[0]; | |
| } | |
| x += max_size + margin; | |
| } | |
| this.setDirtyCanvas(true, true); | |
| }).apply(app.graph); | |
| }, | |
| }); | |
| return options; | |
| }; | |
| }, | |
| }); | |