Cozum-4B
Cozum-4B is a custom-trained, 4 billion parameter language model specifically optimized for Turkish cultural context, Turkish instruction following, and mathematical problem-solving. Built upon the Qwen architecture, it is fine-tuned to go beyond simple translation, providing responses that feel natural, culturally aware, and highly accurate for native Turkish speakers.
Model Details
- Architecture: Qwen (4B parameters)
- License: Apache 2.0
- Language(s): Turkish, English
- Training Framework: LLaMA-Factory
Training Datasets
The model underwent Supervised Fine-Tuning (SFT) using a highly curated mixture of localized and structural datasets. The training pipeline was specifically designed to teach the model local nuances, idioms, and general Turkish culture, alongside robust logical reasoning:
- Aya Turkish Filtered: A specially filtered subset of the Aya dataset, optimized to remove noise while capturing authentic, human-curated Turkish cultural knowledge and natural phrasing.
- Aya Dataset: A comprehensive, multilingual instruction fine-tuning dataset utilized to reinforce broad knowledge and structural capabilities.
- Alpaca Turkish Combined: A combined Turkish instruction dataset designed to improve task completion and ensure the model aligns with user expectations in a localized context.
- GSM8K (Turkish Translation -
gsm8k_tr): High-quality grade-school math word problems translated into Turkish to boost the model's step-by-step mathematical reasoning capabilities without losing language integrity.
Training Configuration
- Method: Supervised Fine-Tuning (SFT)
- Tools Used: LLaMA-Factory
- Model Format: Safetensors (Sharded for optimized loading)
Usage
The model is fully equipped with its necessary tokenizer and processor configuration files. You can load it directly using the transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Yigit-Karaman/Cozum-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
# Example prompting for cultural knowledge
messages = [
{"role": "user", "content": "Bana Türk kahvesi kültürünün ve 'bir fincan kahvenin kırk yıl hatırı vardır' sözünün anlamını açıklar mısın?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))