Instructions to use pthinc/prettybird_bce_basic_vl_3b_q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use pthinc/prettybird_bce_basic_vl_3b_q4 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_vl_3b_q4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pthinc/prettybird_bce_basic_vl_3b_q4: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_vl_3b_q4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pthinc/prettybird_bce_basic_vl_3b_q4:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf pthinc/prettybird_bce_basic_vl_3b_q4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf pthinc/prettybird_bce_basic_vl_3b_q4:Q4_K_M

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_vl_3b_q4:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use pthinc/prettybird_bce_basic_vl_3b_q4 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_vl_3b_q4"
# 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_vl_3b_q4",
		"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_vl_3b_q4:Q4_K_M

Ollama
How to use pthinc/prettybird_bce_basic_vl_3b_q4 with Ollama:
```
ollama run hf.co/pthinc/prettybird_bce_basic_vl_3b_q4:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use pthinc/prettybird_bce_basic_vl_3b_q4 with Docker Model Runner:
```
docker model run hf.co/pthinc/prettybird_bce_basic_vl_3b_q4:Q4_K_M
```

Lemonade

How to use pthinc/prettybird_bce_basic_vl_3b_q4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/prettybird_bce_basic_vl_3b_q4:Q4_K_M

Run and chat with the model

lemonade run user.prettybird_bce_basic_vl_3b_q4-Q4_K_M

List all available models

lemonade list

Atomic Chat

Welcome Real AI Evolution

by prometechinc - opened Dec 10, 2025

Discussion

prometechinc

PROMETECH BİLGİSAYAR BİLİMLERİ YAZILIM İTHALAT İHRACAT TİCARET ANONİM ŞİRKETİ org Dec 10, 2025

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BCE architecture (Behavioral Contextual Encoding, or by other names in current literature) is a holistic behavioral-functional approach that synthesizes human behavior and cognition with today’s algorithmic systems. This report presents a comprehensive analysis of all the core technical, philosophical, and cognitive components of BCE architecture, covering definitions from past literature, current application examples, module recommendations, GitHub structure configurations, consistency and reality checks, ethical filtering, and character maps. Each main heading is detailed with relevant definitions, formulas, algorithmic processes, cognitive background, and examples. Download and experience the real power.

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