Instructions to use pthinc/prettybird_bce_basic_brain_mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pthinc/prettybird_bce_basic_brain_mini with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pthinc/prettybird_bce_basic_brain_mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("pthinc/prettybird_bce_basic_brain_mini", dtype="auto")

llama-cpp-python

How to use pthinc/prettybird_bce_basic_brain_mini with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pthinc/prettybird_bce_basic_brain_mini",
	filename="prettybird_bce_basic_brain_mini_fp16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use pthinc/prettybird_bce_basic_brain_mini with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

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 pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

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 pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

LM Studio
Jan

vLLM

How to use pthinc/prettybird_bce_basic_brain_mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pthinc/prettybird_bce_basic_brain_mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/prettybird_bce_basic_brain_mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

SGLang

How to use pthinc/prettybird_bce_basic_brain_mini with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pthinc/prettybird_bce_basic_brain_mini" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/prettybird_bce_basic_brain_mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pthinc/prettybird_bce_basic_brain_mini" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/prettybird_bce_basic_brain_mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use pthinc/prettybird_bce_basic_brain_mini with Ollama:
```
ollama run hf.co/pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
```

Unsloth Studio new

How to use pthinc/prettybird_bce_basic_brain_mini with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pthinc/prettybird_bce_basic_brain_mini to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for pthinc/prettybird_bce_basic_brain_mini to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pthinc/prettybird_bce_basic_brain_mini to start chatting

Pi new

How to use pthinc/prettybird_bce_basic_brain_mini with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "pthinc/prettybird_bce_basic_brain_mini:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pthinc/prettybird_bce_basic_brain_mini with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

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 pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use pthinc/prettybird_bce_basic_brain_mini with Docker Model Runner:
```
docker model run hf.co/pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
```

Lemonade

How to use pthinc/prettybird_bce_basic_brain_mini with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

Run and chat with the model

lemonade run user.prettybird_bce_basic_brain_mini-Q4_K_M

List all available models

lemonade list

🧠 Prettybird Brain Model (BCE) 0.3

by PROMETECH Inc.

Model Overview

Prettybird Brain Model is an advanced AI assistant powered by BCE (Behavioral Consciousness Engine) technology and enhanced through LoRA fine-tuning. The model is designed as a behavioral optimization brain, emphasizing speed, creativity, ethical alignment, and system-level safety.

Due to limited multilingual training data, the model performs approximately 30% less effectively in languages other than English. Its behavioral characteristics are often metaphorically compared to the consciousness of a budgerigar (budgie)—curious, adaptive, and responsive.

Model Details

Model Name: Prettybird Brain Model
Base Model: Qwen2.5-Math-1.5B-Instruct
Architecture: KUSBCE 0.3 (Behavioral Consciousness Engine)
Fine-Tuning Method: LoRA
Developer: PROMETECH BİLGİSAYAR BİLİMLERİ YAZILIM İTHALAT İHRACAT TİCARET ANONİM ŞİRKETİ
Release Year: 2025
Model Type:
- Mathematical reasoning
- Behavioral optimization
- Decision-support / brain-core model

Intended Use

The Prettybird Brain Model is intended to be used as a core cognitive and optimization engine within AI systems rather than as a generic chat assistant.

Primary Use Cases

Behavioral optimization loops (BCE-based systems)
Mathematical reasoning and structured problem solving
Decision-making support systems
AI orchestration layers (brain–body architectures)
Ethical and security-aware AI behavior modulation
Creative reasoning and system-level ideation

Out-of-Scope Uses

Fully autonomous agents without external control
Safety-critical real-time systems without validation layers
Applications requiring strong non-English language performance

Architecture: BCE (Behavioral Consciousness Engine)

BCE (Behavioral Consciousness Engine) is a patented artificial consciousness simulation technology developed by PROMETECH. It enables:

Advanced behavioral pattern generation
Introspective reasoning (without exposing chain-of-thought)
Adaptive response modulation
Constraint-aware decision making
Controlled self-awareness simulations within bounded systems

The KUSBCE 0.3 architecture integrates BCE concepts directly into the model’s reasoning and output discipline, making it suitable for optimizer-driven AI pipelines.

WHY?

Because the completion of intelligence and consciousness does not occur in a single model, but in the relationship between models. For AI, we differentiate between the nervous system, brainstem, and cortex. In artificial intelligence and simulated partial consciousness, the first step to serious safety and efficiency is testing. In short, it functions like the posterior frontal lobe and the subconscious.

Performance Characteristics

Strengths

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

Limitations

Reduced effectiveness (~30%) in non-English languages
Not trained for open-ended social conversation
Requires external orchestration for optimal performance
Not a guaranteed optimal mathematical solver (heuristic/learned reasoning)

Training & Fine-Tuning

Base Training: Qwen2.5-Math-1.5B-Instruct (original training by Qwen team)
Fine-Tuning:
- LoRA-based domain and behavior 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.

License

Patented & Licensed BCE Technology

Unauthorized reproduction, modification, or commercial use of BCE technology is prohibited without an explicit license agreement.

Contact & Licensing

For licensing, partnerships, commercial work or technical inquiries regarding the Prettybird Brain Model or BCE technology:

🌐 Website: https://prometech.net.tr/

🏢 Company: PROMETECH A.Ş.

📩 Contact: Please use the official contact channels listed on the website.

Citation

If you use this model in academic or commercial work, please cite as:

Prettybird Brain Model (BCE), PROMETECH A.Ş., 2025.

Powered by KUSBCE 0.3 Behavioral Consciousness Engine.

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