Instructions to use suayptalha/Sungur-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suayptalha/Sungur-9B with Transformers:

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

pipe = pipeline("text-generation", model="suayptalha/Sungur-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("suayptalha/Sungur-9B")
model = AutoModelForCausalLM.from_pretrained("suayptalha/Sungur-9B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use suayptalha/Sungur-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suayptalha/Sungur-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suayptalha/Sungur-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/suayptalha/Sungur-9B

SGLang

How to use suayptalha/Sungur-9B 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 "suayptalha/Sungur-9B" \
    --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": "suayptalha/Sungur-9B",
		"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 "suayptalha/Sungur-9B" \
        --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": "suayptalha/Sungur-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use suayptalha/Sungur-9B with Docker Model Runner:
```
docker model run hf.co/suayptalha/Sungur-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Sungur-9B

Sungur-9B is a Turkish-specialized large language model derived from ytu-ce-cosmos/Turkish-Gemma-9b-v0.1, which itself is based on Gemma-2-9b. The model was further trained using a 7k-sample Direct Preference Optimization (DPO) dataset created via translation and fine-tuned with 4-bit QLoRA, refining its alignment with human preferences.

Sungur-9B is designed for Turkish text generation tasks, producing coherent and contextually appropriate outputs. Its training process enables it to deliver fluent, context-aware responses.

Turkish Evaluation Benchmark Results (via `malhajar17/lm-evaluation-harness_turkish`)

Task / Dataset	suayptalha/Sungur-9B	Qwen/Qwen2.5-7B-Instruct	google/gemma-2-9b-it	ytu-ce-cosmos/Turkish-Gemma-9b-v0.1	google/gemma-3-12b-it	Qwen/Qwen2.5-14B-it	Qwen/Qwen2.5-32B-Instruct	google/gemma-2-27b-it	google/gemma-3-27b-it	Qwen/Qwen2.5-72B-Instruct	meta-llama/Llama-3-1-70B-Instruct
MMLU (tr)	61.19	56.31	61.07	63.85	63.92	65.28	70.93	66.49	70.20	77.28	74.00
Truthful_QA (tr)	55.21	55.99	55.77	54.21	57.16	59.00	57.87	57.45	57.06	59.86	51.41
ARC (tr)	55.03	42.06	56.31	59.64	60.67	50.00	57.00	63.65	66.98	61.52	59.64
Hellaswag (tr)	64.36	44.71	56.48	64.19	62.00	52.22	57.04	63.86	66.58	61.98	64.31
Gsm8K (tr)	74.49	64.16	63.10	73.42	72.06	76.77	77.83	76.54	77.52	83.60	66.13
Winogrande (tr)	63.43	59.66	62.09	64.53	61.77	58.77	61.77	65.40	65.80	61.92	66.90

Usage

Pipeline

from transformers import pipeline

pipe = pipeline("text-generation", model="suayptalha/Sungur-9B")

messages = [
    {"role": "user", "content": "Bana kuantum dolanıklığını çok kısaca anlat."},
]
pipe(messages)[0]["generated_text"][-1]["content"]

#Kuantum dolanıklığı, birbirine bağlı iki parçacığın, ne kadar uzakta olsalar bile, birinin durumunun diğerini anında etkilemesi olarak düşünülebilir.

#Örneğin, bir parçacık "yukarı" döndüğünde, dolanık olduğu diğer parçacık kesinlikle "aşağı" dönecektir. Bu değişim anında gerçekleşir, ışık hızını aşarak. Fakat bu durum, uzaktaki parçacığın bir "bilgiyi" aldığını göstermez, çünkü ölçüm sonucu zaten önceden belirlenmiştir.

#Dolanıklık, kuantum dünyasının tuhaf ve ilginç bir özelliğidir ve bilgi teknolojileri (kuantum bilgisayarlar) gibi alanlarda devrim yaratma potansiyeline sahiptir.

AutoModelForCausalLM

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "suayptalha/Sungur-9B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "5x + 1 = 16. x'i bul."},
]

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=2048,
    do_sample=False,
    eos_token_id=tokenizer.eos_token_id
)

output_ids = outputs[0]
input_length = inputs["input_ids"].shape[1]
generated_tokens = output_ids[input_length:]
answer = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(answer)

# 5x + 1 = 16 denklemini çözmek için şu adımları izleyelim:

# 1. **Sabit terimi eşitliğin diğer tarafına taşıyalım:**
#    5x = 16 - 1
#    5x = 15

# 2. **x'i yalnız bırakmak için her iki tarafı 5'e bölelim:**
#    x = 15 / 5
#    x = 3

# **Sonuç:** x = 3

# Denklemi kontrol edelim:
# 5 * 3 + 1 = 15 + 1 = 16 (Doğru)

Acknowledgments

Thanks to ytu-ce-cosmos for their amazing Turkish-Gemma-9b-v0.1 model.
Thanks to axolotl for making the repository I used to make this model.
Thanks to all Turkish open source AI community.

Citation

@misc{sungur_collection_2025,
  title        = {Sungur (Hugging Face Collection)},
  author       = {Şuayp Talha Kocabay},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/collections/suayptalha/sungur-68dcd094da7f8976cdc5898e}},
  note         = {Turkish LLM family and dataset collection}
}