Instructions to use mustafakemal0146/kemu-gpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mustafakemal0146/kemu-gpt with Transformers:

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

pipe = pipeline("text-generation", model="mustafakemal0146/kemu-gpt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mustafakemal0146/kemu-gpt", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mustafakemal0146/kemu-gpt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mustafakemal0146/kemu-gpt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mustafakemal0146/kemu-gpt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mustafakemal0146/kemu-gpt

SGLang

How to use mustafakemal0146/kemu-gpt 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 "mustafakemal0146/kemu-gpt" \
    --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": "mustafakemal0146/kemu-gpt",
		"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 "mustafakemal0146/kemu-gpt" \
        --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": "mustafakemal0146/kemu-gpt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mustafakemal0146/kemu-gpt with Docker Model Runner:
```
docker model run hf.co/mustafakemal0146/kemu-gpt
```

KEMU-GPT

KEMU-GPT is an advanced language model based on GPT-2 Medium and focused entirely on the English language. It is an AI without any subject limitations; it is designed to answer any question you can think of

Model Details

Base Model: GPT-2 Medium
Parameters: ~355M
Training: LoRA fine-tuning
Languages: English
Size: ~1.5GB

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mustafakemal0146/kemu-gpt")
model = AutoModelForCausalLM.from_pretrained("mustafakemal0146/kemu-gpt")

# Generate text
prompt = "Merhaba, nasılsın?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Data

The model was fine-tuned on a curated dataset focusing on:

English philosophy books
General knowledge questions
Helpful assistant responses

Limitations

This model is for educational and research purposes
May generate biased or inappropriate content
Users should verify important information

License

MIT License - see LICENSE file for details.

Citation

@misc{kemu-gpt,
  title={KEMU-GPT: A Fine-tuned English Language Model},
  author={Mustafa Kemal Çıngıl},
  year={2025},
  url={https://huggingface.co/mustafakemal0146/kemu-gpt}
}

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