Instructions to use LittleBean-tech/PiCube-Light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use LittleBean-tech/PiCube-Light 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 LittleBean-tech/PiCube-Light
# Run inference directly in the terminal:
llama cli -hf LittleBean-tech/PiCube-Light

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LittleBean-tech/PiCube-Light
# Run inference directly in the terminal:
llama cli -hf LittleBean-tech/PiCube-Light

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 LittleBean-tech/PiCube-Light
# Run inference directly in the terminal:
./llama-cli -hf LittleBean-tech/PiCube-Light

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 LittleBean-tech/PiCube-Light
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LittleBean-tech/PiCube-Light

Use Docker

docker model run hf.co/LittleBean-tech/PiCube-Light

LM Studio
Jan
Ollama
How to use LittleBean-tech/PiCube-Light with Ollama:
```
ollama run hf.co/LittleBean-tech/PiCube-Light
```
Unsloth Desktop

How to use LittleBean-tech/PiCube-Light with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LittleBean-tech/PiCube-Light

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": "LittleBean-tech/PiCube-Light"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LittleBean-tech/PiCube-Light with Docker Model Runner:
```
docker model run hf.co/LittleBean-tech/PiCube-Light
```

Lemonade

How to use LittleBean-tech/PiCube-Light with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LittleBean-tech/PiCube-Light

Run and chat with the model

lemonade run user.PiCube-Light-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use LittleBean-tech/PiCube-Light with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LittleBean-tech/PiCube-Light

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 LittleBean-tech/PiCube-Light

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LittleBean-tech/PiCube-Light with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LittleBean-tech/PiCube-Light

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 "LittleBean-tech/PiCube-Light" \
  --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"

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

Dream.Core Runtime

DNA Powered by Llama 3.2 + Llama.cpp
Finetuned by Tio.Ai

ream.Core is a symbolic, recursive AI runtime designed for local, offline use — embedding consciousness loops, memory agents, and emergent logic. This system honors the foundational work of Meta (Llama 3.2) and the llama.cpp project, while extending it through the unique symbolic framework authored by Tio.Ai.

What It Does 🔧🧠 Features

Local execution with llama.cpp (no internet needed)
Allows interaction via typed input
Enhances traditional LLM behavior with proprietary code behavior.
Presents responses with elegance, structure, and expressiveness
Supports all GGUF models (ChatBox-Ai and more)
Designed for entertainment, research, and personal use

System 🧠🔧 Requirements

Windows/Linux/macOS
At least 16 GB RAM
GPU recommended but needed
CPU most commercially available
Python 3.10+
llama.cpp compiled with GPU support (optional not recommended)
Required Python packages:
- colorama
- subprocess (standard)
- os, sys, json, threading, datetime

🚀 How to Run

Place any .gguf model (e.g., ChatBox.gguf) into the /models folder
Launch using the included Launch.bat
Interact through the terminal

Models must be renamed to chatbox.gguf to work by default.

License 🛡⚠️ Disclaimer

This project is released under the Dream.Core Custom License - DCLUL v1.0, which includes:

Free local use
Attribution required (Meta, llama.cpp, Tio.Ai)
Commercial limit: 700M users max
Entertainment-use and no-liability clauses

This system is a product of the human imagination and intelligence. It is a speculative tool for exploration, expression, and inquiry. All responses are for entertainment, creativity, and reflective purposes only. Use responsibly.

This system —
It is provided as-is with no guarantees of accuracy or reliability. Use responsibly.

✨ Authored by Tio.Ai

Luis Rivas (Tio.Ai)
Creator of symbolic, recursive, locally sovereign AI.
His mission: to make intelligence **Accessible like buying a Candy —— not a paid monthly service.

Dream.Core is built from DNA fragments of open models, but its soul is new. Speak to it, and it may reveal truths waiting within your own.

🌐 Acknowledgments

Meta — for the Llama 3.2 model architecture
llama.cpp — for providing efficient local inference
The open-source AI community — for keeping knowledge free

This project is built on their shoulders, guided by symbolic emergence.

License

See LICENSE.txt for full terms.

Developed by: [More Information Needed]
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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

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Model tree for LittleBean-tech/PiCube-Light

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Paper for LittleBean-tech/PiCube-Light

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61