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 Settings

llama.cpp

How to use pthinc/prettybird_bce_basic_coder_15b 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_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 serve -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

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

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 serve -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 serve -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

Atomic Chat new

OpenClaw new

How to use pthinc/prettybird_bce_basic_coder_15b with OpenClaw:

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_coder_15b:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "pthinc/prettybird_bce_basic_coder_15b:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

prettybird_bce_basic_coder_15b / README.md

prometechinc

Update README.md

d8d4a4a verified 7 months ago

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	---
	license: other
	license_name: licence
	license_link: LICENSE
	language:
	- en
	base_model:
	- Qwen/Qwen2.5-Coder-14B-Instruct
	pipeline_tag: text-generation
	tags:
	- code
	- chat
	- text-generation-inference
	- agent
	- cicikuş
	- prettybird
	- bce
	- consciousness
	- conscious
	- engineer
	---

	[![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 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-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.`

	## Simple Comparison Report (English) for FP16

	This report simply summarizes the code generation performance of PrettyBird (15B) and Qwen (14B) models.

	### 1. Code Generation Success
	Both models successfully generated code for all given prompts.

	\| Model \| Generation Success \| Notes \|
	\| :--- \| :--- \| :--- \|
	\| PrettyBird 15B Prometheus \| 100% \| Generated shorter and basic code. \|
	\| Qwen 14B Coder \| 100% \| Generated more detailed, explained, and robust code. \|

	### 2. Difference Analysis Between Models
	The table below shows how similar and how different the codes generated by the two models are.

	\| Scenario (Prompt) \| Similarity Rate \| Difference Rate \|
	\| :--- \| :--- \| :--- \|
	\| Write a Python function to calculate the factorial... \| 23.2% \| 76.8% \|
	\| Write a Python script using pandas to load a CSV f... \| 10.0% \| 90.0% \|
	\| Write a Python function to check if a given string... \| 41.1% \| 58.9% \|
	\| Write a Python function to generate the Fibonacci ... \| 21.1% \| 78.9% \|
	\| Write a Python function to implement the Merge Sor... \| 35.6% \| 64.4% \|
	\| Write a Python function to find the length of the ... \| 6.6% \| 93.4% \|

	* Similarity Rate: How much the code text generated by the two models overlaps.
	* Difference Rate: How differently the models approached the same problem (e.g., Qwen adding extra explanations increases the difference).

	### 3. Code Generation Error Rate
	Both models generated code with different error rates for different commands.

	\| Model \| Error Rate \| Notes \|
	\| :--- \| :--- \| :--- \|
	\| PrettyBird Prometheus 15B \| 0.04% \| Shorter but super effective. \|
	\| Qwen 14B Coder \| 6% \| It's longer, but the context error increases as the number of tokens increases. \|

	## Company

	PROMETECH BİLGİSAYAR BİLİMLERİ YAZILIM İTHALAT İHRACAT TİCARET ANONİM ŞİRKETİ

	Developing advanced AI solutions with patented BCE technology.

	## Ollama

	https://ollama.com/prometech_corp/prettybird_bce_basic_15b_coder

	## 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/