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: 2,633 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 | import { app } from "../../../scripts/app.js";
import { ComfyWidgets } from "../../../scripts/widgets.js";
// Displays input text on a node
// TODO: This should need to be so complicated. Refactor at some point.
app.registerExtension({
name: "pysssss.ShowText",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name === "ShowText|pysssss") {
function populate(text) {
if (this.widgets) {
// On older frontend versions there is a hidden converted-widget
const isConvertedWidget = +!!this.inputs?.[0].widget;
for (let i = isConvertedWidget; i < this.widgets.length; i++) {
this.widgets[i].onRemove?.();
}
this.widgets.length = isConvertedWidget;
}
const v = [...text];
if (!v[0]) {
v.shift();
}
for (let list of v) {
// Force list to be an array, not sure why sometimes it is/isn't
if (!(list instanceof Array)) list = [list];
for (const l of list) {
const w = ComfyWidgets["STRING"](this, "text_" + this.widgets?.length ?? 0, ["STRING", { multiline: true }], app).widget;
w.inputEl.readOnly = true;
w.inputEl.style.opacity = 0.6;
w.value = l;
}
}
requestAnimationFrame(() => {
const sz = this.computeSize();
if (sz[0] < this.size[0]) {
sz[0] = this.size[0];
}
if (sz[1] < this.size[1]) {
sz[1] = this.size[1];
}
this.onResize?.(sz);
app.graph.setDirtyCanvas(true, false);
});
}
// When the node is executed we will be sent the input text, display this in the widget
const onExecuted = nodeType.prototype.onExecuted;
nodeType.prototype.onExecuted = function (message) {
onExecuted?.apply(this, arguments);
populate.call(this, message.text);
};
const VALUES = Symbol();
const configure = nodeType.prototype.configure;
nodeType.prototype.configure = function () {
// Store unmodified widget values as they get removed on configure by new frontend
this[VALUES] = arguments[0]?.widgets_values;
return configure?.apply(this, arguments);
};
const onConfigure = nodeType.prototype.onConfigure;
nodeType.prototype.onConfigure = function () {
onConfigure?.apply(this, arguments);
const widgets_values = this[VALUES];
if (widgets_values?.length) {
// In newer frontend there seems to be a delay in creating the initial widget
requestAnimationFrame(() => {
populate.call(this, widgets_values.slice(+(widgets_values.length > 1 && this.inputs?.[0].widget)));
});
}
};
}
},
});
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