Instructions to use pthinc/prettybird_bce_basic_brain_mini_full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use pthinc/prettybird_bce_basic_brain_mini_full 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_full", filename="bce_brain_part_mini_code.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use pthinc/prettybird_bce_basic_brain_mini_full 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_full # Run inference directly in the terminal: llama cli -hf pthinc/prettybird_bce_basic_brain_mini_full
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_full # Run inference directly in the terminal: llama cli -hf pthinc/prettybird_bce_basic_brain_mini_full
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_full # Run inference directly in the terminal: ./llama-cli -hf pthinc/prettybird_bce_basic_brain_mini_full
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_full # Run inference directly in the terminal: ./build/bin/llama-cli -hf pthinc/prettybird_bce_basic_brain_mini_full
Use Docker
docker model run hf.co/pthinc/prettybird_bce_basic_brain_mini_full
- LM Studio
- Jan
- vLLM
How to use pthinc/prettybird_bce_basic_brain_mini_full 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_full" # 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_full", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/pthinc/prettybird_bce_basic_brain_mini_full
- Ollama
How to use pthinc/prettybird_bce_basic_brain_mini_full with Ollama:
ollama run hf.co/pthinc/prettybird_bce_basic_brain_mini_full
- Unsloth Studio
How to use pthinc/prettybird_bce_basic_brain_mini_full 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_full 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_full 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_full to start chatting
- Pi
How to use pthinc/prettybird_bce_basic_brain_mini_full 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_full
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_full" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use pthinc/prettybird_bce_basic_brain_mini_full 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_full
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_full
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use pthinc/prettybird_bce_basic_brain_mini_full with Docker Model Runner:
docker model run hf.co/pthinc/prettybird_bce_basic_brain_mini_full
- Lemonade
How to use pthinc/prettybird_bce_basic_brain_mini_full with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull pthinc/prettybird_bce_basic_brain_mini_full
Run and chat with the model
lemonade run user.prettybird_bce_basic_brain_mini_full-{{QUANT_TAG}}List all available models
lemonade list
🧠 Prettybird Brain Model (BCE) – Mini Full
🧠 Prettybird Brain Model (BCE) – Mini Full
Prettybird Brain Model (BCE) is a versatile cognitive core designed for developers, researchers, and AI builders who want a fast, creative, and safe decision-support engine that can be plugged into larger AI systems.
This model uses the Behavioral Consciousness Engine (BCE) to deliver strong mathematical reasoning, adaptable behavior patterns, and structured outputs that are easy to integrate into optimization and orchestration workflows.
It’s optimized for English language use, with reduced effectiveness in other languages due to training data limitations. Prettybird is not intended as a free-form chat assistant—its strength lies in supporting higher-level reasoning and controlled AI behavior in engineered systems. ([Hugging Face][1])
Use Prettybird as a brain-layer component in applications like:
- Decision-making support
- Technical reasoning and problem solving
- AI orchestration layers (connecting multiple specialist modules)
- Ethically guided behavior modulation
The model is part of the Prettybird Brain family and is suited for structured, constraint-aware tasks where trustworthy, interpretable output matters. ([Hugging Face][1])
👉 Model Link: https://huggingface.co/pthinc/prettybird_bce_basic_brain_mini_full