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:
coloramasubprocess (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]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
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
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
