Instructions to use pthinc/prettybird_bce_basic_coder_8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pthinc/prettybird_bce_basic_coder_8b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-coder-7b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "pthinc/prettybird_bce_basic_coder_8b")

Transformers

How to use pthinc/prettybird_bce_basic_coder_8b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pthinc/prettybird_bce_basic_coder_8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pthinc/prettybird_bce_basic_coder_8b")
model = AutoModelForCausalLM.from_pretrained("pthinc/prettybird_bce_basic_coder_8b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use pthinc/prettybird_bce_basic_coder_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_coder_8b",
	filename="prettybird_bce_basic_coder_8b.q2_k.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_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_coder_8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_coder_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_coder_8b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/prettybird_bce_basic_coder_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_coder_8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pthinc/prettybird_bce_basic_coder_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_coder_8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

LM Studio
Jan

vLLM

How to use pthinc/prettybird_bce_basic_coder_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_coder_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_coder_8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

SGLang

How to use pthinc/prettybird_bce_basic_coder_8b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pthinc/prettybird_bce_basic_coder_8b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/prettybird_bce_basic_coder_8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pthinc/prettybird_bce_basic_coder_8b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/prettybird_bce_basic_coder_8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use pthinc/prettybird_bce_basic_coder_8b with Ollama:
```
ollama run hf.co/pthinc/prettybird_bce_basic_coder_8b:Q4_K_M
```

Unsloth Studio new

How to use pthinc/prettybird_bce_basic_coder_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_coder_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_coder_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_coder_8b to start chatting

Pi new

How to use pthinc/prettybird_bce_basic_coder_8b 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_8b: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_8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use pthinc/prettybird_bce_basic_coder_8b 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_8b: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_8b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use pthinc/prettybird_bce_basic_coder_8b with Docker Model Runner:
```
docker model run hf.co/pthinc/prettybird_bce_basic_coder_8b:Q4_K_M
```

Lemonade

How to use pthinc/prettybird_bce_basic_coder_8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/prettybird_bce_basic_coder_8b:Q4_K_M

Run and chat with the model

lemonade run user.prettybird_bce_basic_coder_8b-Q4_K_M

List all available models

lemonade list

🦜 Prettybird BCE Basic Coder 8B

by prometechinc - opened Dec 20, 2025

Discussion

prometechinc

PROMETECH BİLGİSAYAR BİLİMLERİ YAZILIM İTHALAT İHRACAT TİCARET ANONİM ŞİRKETİ org Dec 20, 2025

•

edited Dec 20, 2025

🦜 Prettybird BCE Basic Coder 8B

Prettybird BCE Basic Coder 8B is an advanced AI coding and conversational assistant model developed by PROMETECH.
It is designed to support software development workflows with strong code generation, reasoning, and structured responses.

The model is built on top of a modern coder-optimized large language model and enhanced using the BCE (Behavioral Consciousness Engine) approach, which focuses on improved behavioral consistency, contextual awareness, and adaptive reasoning.

🔹 Key Features

Model Type: Code generation & conversational AI
Parameters: ~8B
Architecture: BCE-enhanced coder model
Fine-tuning: LoRA-based behavioral and response optimization
Primary Language: English
Output Style: Structured, deterministic, and developer-friendly

🚀 Capabilities

Code generation, completion, and explanation
Programming-focused conversational assistance
Context-aware reasoning for engineering tasks
Stable and controlled outputs suitable for production use

🧠 About BCE (Behavioral Consciousness Engine)

BCE is an experimental design philosophy aimed at improving behavioral coherence, introspection-like reasoning, and response stability, enabling more natural and reliable interactions compared to standard fine-tuned models.

✅ Intended Use

Prettybird BCE Basic Coder 8B is intended for:

Developer assistants
Coding Q&A systems
AI-powered programming tools
Research and experimentation in behavior-aware AI models

Model: https://huggingface.co/pthinc/prettybird_bce_basic_coder_8b

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