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

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

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"

20260718

File size: 998 Bytes

f859a6f

import { app } from "../../../scripts/app.js";
import { ComfyWidgets } from "../../../scripts/widgets.js";

// Displays input text on a node

app.registerExtension({
	name: "pysssss.StringFunction",
	async beforeRegisterNodeDef(nodeType, nodeData, app) {
		if (nodeData.name === "StringFunction|pysssss") {
			const onExecuted = nodeType.prototype.onExecuted;
			nodeType.prototype.onExecuted = function (message) {
				onExecuted?.apply(this, arguments);

				if (this.widgets) {
					const pos = this.widgets.findIndex((w) => w.name === "result");
					if (pos !== -1) {
						for (let i = pos; i < this.widgets.length; i++) {
							this.widgets[i].onRemove?.();
						}
						this.widgets.length = pos;
					}
				}

				const w = ComfyWidgets["STRING"](this, "result", ["STRING", { multiline: true }], app).widget;
				w.inputEl.readOnly = true;
				w.inputEl.style.opacity = 0.6;
				w.value = message.text;

				this.onResize?.(this.size);
			};
		}
	},
});