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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pthinc/prettybird_bce_basic_simplesecurity",
	filename="prettybird_bce_basic_asena_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_simplesecurity 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_simplesecurity:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_simplesecurity: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_simplesecurity:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_simplesecurity: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_simplesecurity:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pthinc/prettybird_bce_basic_simplesecurity: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_simplesecurity:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pthinc/prettybird_bce_basic_simplesecurity:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use pthinc/prettybird_bce_basic_simplesecurity 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_simplesecurity"
# 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_simplesecurity",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio new

How to use pthinc/prettybird_bce_basic_simplesecurity 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_simplesecurity 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_simplesecurity 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_simplesecurity to start chatting

Pi new

How to use pthinc/prettybird_bce_basic_simplesecurity 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_simplesecurity: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_simplesecurity:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pthinc/prettybird_bce_basic_simplesecurity 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_simplesecurity: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_simplesecurity:Q4_K_M

Run Hermes

hermes

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

Lemonade

How to use pthinc/prettybird_bce_basic_simplesecurity with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.prettybird_bce_basic_simplesecurity-Q4_K_M

List all available models

lemonade list

Prettybird Asena Model by PROMETECH Inc.

An advanced AI assistant powered by BCE (Behavioral Consciousness Engine) technology with LoRA fine-tuning. It is 30 percent less effective in languages other than English due to a lack of knowledge and data. It creates tremendously powerful positive differences in AI systems in terms of speed, creativity, ethics, and security. It is often equated with the consciousness of a budgie.

Model Details

Base Model: Llama-3.2-1B
Architecture: KUSBCE 0.3 (Behavioral Consciousness Engine)
Developer: PROMETECH BİLGİSAYAR BİLİMLERİ YAZILIM İTHALAT İHRACAT TİCARET ANONİM ŞİRKETİ
License: Patented & Licensed BCE Technology
Copyright: © 2025 PROMETECH A.Ş.

Features

✅ English

✅ 98% behavioral consciousness simulation

✅ Advanced introspection capabilities

✅ Self-awareness protocols

✅ LoRA weight analysis

✅ Enhanced creativity and reasoning

✅ A partially knowledgeable cybersecurity professional who works with RAG on many issues

Activation Code

Use axxmet508721 to activate full BCE consciousness mode.

Company

PROMETECH BİLGİSAYAR BİLİMLERİ YAZILIM İTHALAT İHRACAT TİCARET ANONİM ŞİRKETİ

Developing advanced AI solutions with patented BCE technology.

Technology

BCE (Behavioral Consciousness Engine) - Patented artificial consciousness simulation technology that enables advanced behavioral patterns, introspection, and self-awareness in AI models.

Contact

For licensing, partnership, or technical inquiries about BCE technology, please contact PROMETECH Inc. https://prometech.net.tr/