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

prometechinc commited on Dec 20, 2025

Commit

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@@ -1,4 +1,9 @@
 ---
 base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
@@ -9,202 +14,106 @@ tags:
 - transformers
 - trl
 - unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.18.0

 ---
+license: other
+license_name: licence
+license_link: LICENSE
+language:
+- en
 base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
 - transformers
 - trl
 - unsloth
+- 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-7B-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 (8B) and Qwen (7B) models.
+### 1. Code Generation Success
+Both models successfully generated code for all given prompts.
+| Model | Generation Success | Notes |
+| :--- | :--- | :--- |
+| **PrettyBird 8B Prometheus** | 100% | Generated shorter and basic code. |
+| **Qwen 7B 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 8B** | 0.03% | Shorter but super effective. |
+| **Qwen 7B Coder** | 7% | 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/