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"
File size: 2,981 Bytes
f859a6f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | 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;
};
},
});
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