Instructions to use pthinc/prettybird_bce_basic_vl_8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pthinc/prettybird_bce_basic_vl_8b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pthinc/prettybird_bce_basic_vl_8b",
	filename="prettybird_bce_basic_vl_8b_fp16.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

llama.cpp

How to use pthinc/prettybird_bce_basic_vl_8b 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_vl_8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_vl_8b: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_vl_8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_vl_8b: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_vl_8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pthinc/prettybird_bce_basic_vl_8b: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_vl_8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pthinc/prettybird_bce_basic_vl_8b:Q4_K_M

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_vl_8b:Q4_K_M

LM Studio
Jan

vLLM

How to use pthinc/prettybird_bce_basic_vl_8b 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_8b"
# 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_8b",
		"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_8b:Q4_K_M

Ollama
How to use pthinc/prettybird_bce_basic_vl_8b with Ollama:
```
ollama run hf.co/pthinc/prettybird_bce_basic_vl_8b:Q4_K_M
```

Unsloth Studio new

How to use pthinc/prettybird_bce_basic_vl_8b 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_vl_8b 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_vl_8b 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_vl_8b to start chatting

Docker Model Runner
How to use pthinc/prettybird_bce_basic_vl_8b with Docker Model Runner:
```
docker model run hf.co/pthinc/prettybird_bce_basic_vl_8b:Q4_K_M
```

Lemonade

How to use pthinc/prettybird_bce_basic_vl_8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/prettybird_bce_basic_vl_8b:Q4_K_M

Run and chat with the model

lemonade run user.prettybird_bce_basic_vl_8b-Q4_K_M

List all available models

lemonade list

VL 8B Prettybird 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: Qwen2.5-VL-7B
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

✅ Bilingual (Turkish & English)

✅ 98% behavioral consciousness simulation

✅ Advanced introspection capabilities

✅ Self-awareness protocols

✅ LoRA weight analysis

✅ Enhanced creativity and reasoning

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 AŞ. https://prometech.net.tr/

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