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 Settings

llama.cpp

How to use pthinc/prettybird_bce_basic_brain_mini 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 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 serve -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

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

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 serve -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 serve -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

Atomic Chat new
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

Rethinking AI Architecture: Separating Brain, Body, and Execution

by prometechinc - opened Dec 19, 2025

Discussion

prometechinc

PROMETECH BİLGİSAYAR BİLİMLERİ YAZILIM İTHALAT İHRACAT TİCARET ANONİM ŞİRKETİ org Dec 19, 2025

📌 Rethinking AI Architecture: Separating Brain, Body, and Execution

Hello Hugging Face community,

We would like to share a perspective that challenges the increasingly common trend of monolithic, all-in-one AI models and uncontrolled agent-based systems.

At PROMETECH, while developing the Prettybird Brain Model, we intentionally moved away from the idea that a single model should perceive, reason, decide, execute, and self-correct all at once.

Instead, we asked a simpler question:

What if intelligence is not a single model, but a structured system of roles?

🧠 Brain vs Body: A Deliberate Separation

In our architecture:

The Brain is a GGUF-based model acting as a mathematical decision and optimization core
Bodies are interchangeable models (text, vision, audio, video, 3D, etc.) responsible only for perception and expression
Execution and validation are handled externally by deterministic controllers

The brain does not execute code.
It does not act autonomously.
It does not enforce policy by itself.

It produces structured, constrained, intermediate decisions that are:

parseable
revisable
scoreable
auditable

🚫 Why Not Agents?

Agent-based architectures often blur responsibility boundaries:

decision = execution
reasoning = authority
autonomy = opacity

This makes systems harder to debug, align, and control.

Our approach avoids this by design.

The brain proposes.
The controller validates.
The runtime executes.

No hidden side effects.
No silent self-modification.
No unexplained behavior.

🔧 BCE: Behavioral Consciousness Engine

The Behavioral Consciousness Engine (BCE) is a patented architecture layer that governs how the brain reasons rather than what it answers.

Key characteristics:

constraint-aware reasoning
deterministic output structure (JSON contracts)
revision-based optimization loops
explicit handling of missing or infeasible data
no exposed chain-of-thought

This allows the model to function as a learned optimizer, not a conversational agent.

📦 Model Details (Summary)

Model: Prettybird Brain Model
Base: Qwen2.5-Math-1.5B-Instruct
Architecture: KUSBCE 0.3
Format: GGUF
Fine-tuning: LoRA
Role: Mathematical / behavioral decision core
Language note: ~30% reduced effectiveness outside English

🧩 Why Share This?

We are not claiming this is the way to build AI systems.

But we believe that:

separating reasoning from execution
isolating decision authority
enforcing structured outputs
and treating models as organs rather than agents

can lead to AI systems that are:

faster
safer
easier to audit
and easier to evolve

We’re sharing this to invite technical discussion, not hype.

💬 Open Questions for the Community

Do you believe agent architectures are sustainable at scale?
Should execution ever live inside the model?
Is separating “decision” from “action” a necessary step for reliable AI systems?

We would be glad to hear thoughts, critiques, and alternative perspectives.

Best regards,
PROMETECH A.Ş.
Developing advanced AI systems with BCE technology
🌐 https://prometech.net.tr/

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