Instructions to use pthinc/prettybird_bce_basic_coder_15b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pthinc/prettybird_bce_basic_coder_15b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pthinc/prettybird_bce_basic_coder_15b",
	filename="prettybird_bce_basic_coder_15b_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_coder_15b 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_coder_15b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_coder_15b: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_coder_15b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_coder_15b: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_coder_15b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pthinc/prettybird_bce_basic_coder_15b: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_coder_15b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pthinc/prettybird_bce_basic_coder_15b:Q4_K_M

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_coder_15b:Q4_K_M

LM Studio
Jan

vLLM

How to use pthinc/prettybird_bce_basic_coder_15b 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_coder_15b"
# 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_coder_15b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_coder_15b:Q4_K_M

Ollama
How to use pthinc/prettybird_bce_basic_coder_15b with Ollama:
```
ollama run hf.co/pthinc/prettybird_bce_basic_coder_15b:Q4_K_M
```

Unsloth Studio new

How to use pthinc/prettybird_bce_basic_coder_15b 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_coder_15b 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_coder_15b 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_coder_15b to start chatting

Pi new

How to use pthinc/prettybird_bce_basic_coder_15b 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_coder_15b: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_coder_15b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pthinc/prettybird_bce_basic_coder_15b 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_coder_15b: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_coder_15b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use pthinc/prettybird_bce_basic_coder_15b with Docker Model Runner:
```
docker model run hf.co/pthinc/prettybird_bce_basic_coder_15b:Q4_K_M
```

Lemonade

How to use pthinc/prettybird_bce_basic_coder_15b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/prettybird_bce_basic_coder_15b:Q4_K_M

Run and chat with the model

lemonade run user.prettybird_bce_basic_coder_15b-Q4_K_M

List all available models

lemonade list

Prometech Computer Sciences Corp commited on Dec 14, 2025

Commit

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1 Parent(s): aa13350

Update README.md

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@@ -1,5 +1,66 @@
----
-license: other
-license_name: licence
-license_link: LICENSE
----

+---
+license: other
+license_name: licence
+license_link: LICENSE
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-14B-Instruct
+tags:
+- code
+- chat
+- text-generation-inference
+- agent
+- cicikuş
+- prettybird
+- bce
+- consciousness
+- conscious
+---
+[![License](https://img.shields.io/badge/License-Proprietary-red.svg)](./licence.md)
+[![Dataset](https://img.shields.io/badge/Dataset-BCE-green.svg)](https://prometech.net.tr)
+Prettybird Prometheus Model by PROMETECH Corp.
+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-Coder-14B-Instruct
+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
+✅ This model is configured with low randomness and controlled context settings to produce accurate, safe, and repeatable outputs in a production environment. It is designed to be compatible with engineering processes, prioritizing traceability, testability, and fault tolerance.
+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 Corp. https://prometech.net.tr/