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: 2,686 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 | import { app } from "../../../scripts/app.js";
app.registerExtension({
name: "pysssss.ContextMenuHook",
init() {
const getOrSet = (target, name, create) => {
if (name in target) return target[name];
return (target[name] = create());
};
const symbol = getOrSet(window, "__pysssss__", () => Symbol("__pysssss__"));
const store = getOrSet(window, symbol, () => ({}));
const contextMenuHook = getOrSet(store, "contextMenuHook", () => ({}));
for (const e of ["ctor", "preAddItem", "addItem"]) {
if (!contextMenuHook[e]) {
contextMenuHook[e] = [];
}
}
// Big ol' hack to get allow customizing the context menu
// Replace the addItem function with our own that wraps the context of "this" with a proxy
// That proxy then replaces the constructor with another proxy
// That proxy then calls the custom ContextMenu that supports filters
const ctorProxy = new Proxy(LiteGraph.ContextMenu, {
construct(target, args) {
return new LiteGraph.ContextMenu(...args);
},
});
function triggerCallbacks(name, getArgs, handler) {
const callbacks = contextMenuHook[name];
if (callbacks && callbacks instanceof Array) {
for (const cb of callbacks) {
const r = cb(...getArgs());
handler?.call(this, r);
}
} else {
console.warn("[pysssss 🐍]", `invalid ${name} callbacks`, callbacks, name in contextMenuHook);
}
}
const addItem = LiteGraph.ContextMenu.prototype.addItem;
LiteGraph.ContextMenu.prototype.addItem = function () {
const proxy = new Proxy(this, {
get(target, prop) {
if (prop === "constructor") {
return ctorProxy;
}
return target[prop];
},
});
proxy.__target__ = this;
let el;
let args = arguments;
triggerCallbacks(
"preAddItem",
() => [el, this, args],
(r) => {
if (r !== undefined) el = r;
}
);
if (el === undefined) {
el = addItem.apply(proxy, arguments);
}
triggerCallbacks(
"addItem",
() => [el, this, args],
(r) => {
if (r !== undefined) el = r;
}
);
return el;
};
// We also need to patch the ContextMenu constructor to unwrap the parent else it fails a LiteGraph type check
const ctxMenu = LiteGraph.ContextMenu;
LiteGraph.ContextMenu = function (values, options) {
if (options?.parentMenu) {
if (options.parentMenu.__target__) {
options.parentMenu = options.parentMenu.__target__;
}
}
triggerCallbacks("ctor", () => [values, options]);
return ctxMenu.call(this, values, options);
};
LiteGraph.ContextMenu.prototype = ctxMenu.prototype;
},
});
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