Instructions to use hipinis/20260718 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use hipinis/20260718 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 hipinis/20260718:Q8_0 # Run inference directly in the terminal: llama cli -hf hipinis/20260718:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf hipinis/20260718:Q8_0 # Run inference directly in the terminal: llama cli -hf hipinis/20260718: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 hipinis/20260718:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf hipinis/20260718: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 hipinis/20260718:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf hipinis/20260718:Q8_0
Use Docker
docker model run hf.co/hipinis/20260718:Q8_0
- LM Studio
- Jan
- Ollama
How to use hipinis/20260718 with Ollama:
ollama run hf.co/hipinis/20260718:Q8_0
- Unsloth Desktop
- Pi
How to use hipinis/20260718 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hipinis/20260718:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "hipinis/20260718:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use hipinis/20260718 with Docker Model Runner:
docker model run hf.co/hipinis/20260718:Q8_0
- Lemonade
How to use hipinis/20260718 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hipinis/20260718:Q8_0
Run and chat with the model
lemonade run user.20260718-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use hipinis/20260718 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hipinis/20260718: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 hipinis/20260718:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use hipinis/20260718 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hipinis/20260718: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 "hipinis/20260718: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"
Download custom_nodes/ComfyUI-Custom-Scripts/web/js/graphArrange.js from hipinis/20260718: direct link, hf CLI and curl.
- Browser
- Download file 2.98 kB
-
https://huggingface.co/hipinis/20260718/resolve/main/custom_nodes/ComfyUI-Custom-Scripts/web/js/graphArrange.js
- Command line
-
hf download hf://hipinis/20260718/custom_nodes/ComfyUI-Custom-Scripts/web/js/graphArrange.js
-
curl -L -o graphArrange.js https://huggingface.co/hipinis/20260718/resolve/main/custom_nodes/ComfyUI-Custom-Scripts/web/js/graphArrange.js
2.98 kB
| 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 | |
| for (let i = nodes.length - 1; i >= 0; i--) { | |
| const node = nodes[i]; | |
| let max = null; | |
| for (const out of node.outputs || []) { | |
| if (out.links) { | |
| for (const link of out.links) { | |
| const outNode = app.graph.getNodeById(app.graph.links[link].target_id); | |
| 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] = []; | |
| } | |
| 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; | |
| }; | |
| }, | |
| }); | |