suayptalha/Sungur-9B-GGUF

Sungur-9B

This is the quantized version of suayptalha/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

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}
}

Support

---

license: gemma2