Turkish-LLM-32B-Instruct

The largest open-source Turkish-enhanced language model. Fine-tuned from Qwen2.5-32B-Instruct with QLoRA on 242K Turkish instruction examples.

Part of the Turkish LLM Family - a complete suite of Turkish language models from 7B to 32B.

Highlights

32B parameters - largest openly available Turkish fine-tuned model
Outperforms base model on MMLU-TR (+0.46) and XNLI-TR (+0.32)
Strong STEM performance - College CS +7.1, College Math +5.0, HS Math +4.1 vs base
GGUF available - Q4/Q5/Q8 quantizations for local inference

Benchmark Results

Benchmark	Base (Qwen2.5-32B)	Ours	Delta
MMLU-TR (57 categories)	0.6518	0.6564	+0.46
XNLI-TR (NLI)	0.4578	0.4610	+0.32
XCOPA-TR (Causal)	0.6800	0.6740	-0.60

MMLU-TR: Strongest Category Improvements

Category	Base	Ours	Delta
College Computer Science	0.545	0.616	+7.1
Logical Fallacies	0.640	0.696	+5.6
College Mathematics	0.530	0.580	+5.0
Formal Logic	0.508	0.556	+4.8
High School Mathematics	0.507	0.548	+4.1

32 of 57 categories improved vs base model.

Quick Start

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "ogulcanaydogan/Turkish-LLM-32B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ogulcanaydogan/Turkish-LLM-32B-Instruct")

messages = [
    {"role": "system", "content": "Sen yardimci bir Turkce asistansin."},
    {"role": "user", "content": "Yapay zekanin saglik sektorundeki uygulamalarini acikla."}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With Ollama (GGUF)

ollama run hf.co/ogulcanaydogan/Turkish-LLM-32B-Instruct-GGUF:Q4_K_M

With vLLM

vllm serve ogulcanaydogan/Turkish-LLM-32B-Instruct --dtype auto --max-model-len 4096

Training Details

Parameter	Value
Base Model	Qwen/Qwen2.5-32B-Instruct
Method	QLoRA (4-bit NF4 + double quantization)
LoRA rank / alpha	32 / 64
Learning rate	1e-5 (cosine schedule)
Epochs	1
Effective batch size	16
Max sequence length	2048
Training time	~3 days on NVIDIA A100 80GB
Dataset	242K Turkish instruction examples

Turkish LLM Family

Model	Size	MMLU-TR	Download
Turkish-LLM-7B-Instruct	7B	-	GGUF
Turkish-LLM-14B-Instruct	14B	0.5977	GGUF
Turkish-LLM-32B-Instruct	32B	0.6564	GGUF

Limitations

Slight regression on XCOPA-TR causal reasoning (-0.6 points, within standard error)
Inherits base model limitations for very long contexts
Best suited for Turkish STEM, reasoning, and general knowledge tasks

Citation

@misc{aydogan2026turkishllm,
  title={Turkish LLM Family: Open-Source Turkish Language Models},
  author={Ogulcan Aydogan},
  year={2026},
  url={https://huggingface.co/collections/ogulcanaydogan/turkish-llm-family-69b303b4ef1c36caffca4e94}
}