🧠 Prettybird Brain Model (BCE) v0.3

by PROMETECH Inc.

Model Overview

Prettybird Brain Model (BCE) is an advanced cognitive core designed for behavioral optimization, mathematical reasoning, and decision-support systems. It is powered by BCE (Behavioral Consciousness Engine) technology and enhanced through LoRA fine-tuning, enabling fast, creative, and ethically aligned reasoning under strict system constraints.

Rather than acting as a generic chat assistant, Prettybird is intended to function as a brain-layer component within larger AI architectures.

Due to limited multilingual training data, performance in non-English languages is approximately 30% lower. Its behavioral profile is often metaphorically compared to a **budgerigar (budgie)**—curious, adaptive, fast-reacting, and constraint-aware.

Model Details

Model Name: Prettybird Brain Model
Base Models:
- Qwen2.5-Math-1.5B-Instruct
- Qwen2.5-1.5B-Instruct
- Qwen2.5-Coder-1.5B-Instruct
- Qwen2.5-VL-3B-Instruct
Architecture: KUSBCE 0.3 (Behavioral Consciousness Engine)
Fine-Tuning Method: LoRA
Developer: PROMETECH A.Ş.
Release Year: 2025
Model Type:
- Mathematical reasoning
- Behavioral optimization
- Decision-support / brain-core model

Intended Use

Prettybird Brain Model is designed to operate as a cognitive and optimization engine, not as a standalone autonomous agent.

Primary Use Cases

BCE-driven behavioral optimization loops
Mathematical and symbolic reasoning
Decision-making support systems
AI orchestration layers (brain–body architectures)
Ethical and security-aware behavior modulation
Creative reasoning under constraints

Out-of-Scope Uses

Fully autonomous agents without external supervision
Safety-critical real-time systems without validation layers
Applications requiring high-level multilingual fluency
Social or entertainment-focused chat systems

BCE Architecture (Behavioral Consciousness Engine)

BCE is a patented artificial consciousness simulation technology developed by PROMETECH. It enables controlled, bounded forms of artificial self-regulation without exposing internal chain-of-thought.

Core capabilities include:

Advanced behavioral pattern generation
Introspective reasoning (non-exposed)
Adaptive response modulation
Constraint-aware decision-making
Simulated self-awareness within supervised systems

KUSBCE 0.3 integrates these concepts directly into the model’s output discipline, making it ideal for optimizer-driven pipelines and multi-model systems.

Philosophy: Intelligence and consciousness do not emerge from a single model, but from relationships between models. Prettybird functions analogously to the posterior frontal lobe and subconscious layer within artificial cognitive systems.

Performance Characteristics

Strengths

High-speed inference and low latency
Strong mathematical and symbolic reasoning
High creativity under strict constraints
Improved ethical and security-aware behavior
Excellent compatibility with external controllers (Python / BCE systems)

Limitations

~30% reduced performance in non-English languages
Not optimized for casual conversation
Requires external orchestration for best results
Heuristic reasoning (not a guaranteed optimal solver)

Training & Fine-Tuning

Base Training: Original Qwen2.5 training by the Qwen team
Fine-Tuning:
- LoRA-based behavioral and domain adaptation
- BCE-aligned behavioral constraints
Data Sources:
- Proprietary datasets
- Mathematical and reasoning-focused corpora
- Behavioral optimization scenarios

Exact training data details are not publicly disclosed due to proprietary BCE technology.

Ethical Design & Safety

Prettybird Brain Model does not assume final decision authority.

Designed to operate under external ethical controllers
Intended for supervised and auditable systems
Encourages structured, machine-parseable outputs
Minimizes hallucination of missing data
Outputs are meant to be validated and corrected externally

Brain Bus Deployment Package

The Brain Bus system enables multi-expert orchestration optimized for T4 GPUs.

Included Components

bce_brain_part_mini_*.gguf — Quantized GGUF models
- normal — General reasoning
- code — Programming specialist
- math — Mathematical reasoning
- vl — Vision-Language (Qwen2.5-VL)
advanced_brain_bus.py — Expert routing orchestrator
Modelfile.* — Ollama configuration files
system_prompts.md — Expert system prompts
cat.png — Sample test image

Setup

ollama create brain-normal -f Modelfile.normal
ollama create brain-code -f Modelfile.code
ollama create brain-math -f Modelfile.math
python advanced_brain_bus.py