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: 1,364 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 | import { app } from "../../../scripts/app.js";
import { ComfyWidgets } from "../../../scripts/widgets.js";
app.registerExtension({
name: "pysssss.MathExpression",
init() {
const STRING = ComfyWidgets.STRING;
ComfyWidgets.STRING = function (node, inputName, inputData) {
const r = STRING.apply(this, arguments);
r.widget.dynamicPrompts = inputData?.[1].dynamicPrompts;
return r;
};
},
beforeRegisterNodeDef(nodeType) {
if (nodeType.comfyClass === "MathExpression|pysssss") {
const onDrawForeground = nodeType.prototype.onDrawForeground;
nodeType.prototype.onNodeCreated = function() {
// These are typed as any to bypass backend validation
// update frontend to restrict types
for(const input of this.inputs) {
input.type = "INT,FLOAT,IMAGE,LATENT";
}
}
nodeType.prototype.onDrawForeground = function (ctx) {
const r = onDrawForeground?.apply?.(this, arguments);
const v = app.nodeOutputs?.[this.id + ""];
if (!this.flags.collapsed && v) {
const text = v.value[0] + "";
ctx.save();
ctx.font = "bold 12px sans-serif";
ctx.fillStyle = "dodgerblue";
const sz = ctx.measureText(text);
ctx.fillText(text, this.size[0] - sz.width - 5, LiteGraph.NODE_SLOT_HEIGHT * 3);
ctx.restore();
}
return r;
};
}
},
});
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