erenfazlioglu/turkish-instruct-reasoning-dpo-3.4m

🇹🇷 Turkish Instruct · Reasoning · DPO — ~3.4M

*The largest open native-Turkish instruction-tuning suite: SFT + chain-of-thought reasoning + DPO preferences, with an independently verified reasoning tier and a unique Turkey-grounded slice.*

En büyük açık native Türkçe talimat-eğitim seti: SFT + adım-adım muhakeme (CoT) + DPO tercih çiftleri; bağımsız doğrulanmış muhakeme katmanı ve Türkiye-temelli özgün dilim içerir.

🌍 In other languages

• —English: ~3.4M native Turkish examples for instruction-tuning (SFT), chain-of-thought reasoning, and DPO preference alignment — with a verified reasoning tier and a Turkey-grounded slice.
• —Türkçe: Talimat-eğitimi, muhakeme ve DPO hizalama için ~3.4M native Türkçe örnek; doğrulanmış muhakeme katmanı + Türkiye-temelli dilim.
• —Español: ~3,4M de ejemplos nativos en turco para instruction-tuning, razonamiento (CoT) y alineación por preferencias (DPO).
• —Français : ~3,4M d'exemples turcs natifs pour l'instruction-tuning, le raisonnement (CoT) et l'alignement par préférences (DPO).
• —Deutsch: ~3,4 Mio. native türkische Beispiele für Instruction-Tuning, Reasoning (CoT) und DPO-Präferenzausrichtung.
• —Русский: ~3,4 млн нативных турецких примеров для instruction-tuning, рассуждений (CoT) и выравнивания по предпочтениям (DPO).
• —中文： 约 340 万条原生土耳其语样本，用于指令微调（SFT）、思维链推理（CoT）和 DPO 偏好对齐。
• —العربية: نحو 3.4 مليون مثال أصلي باللغة التركية للضبط التعليمي والاستدلال التسلسلي ومواءمة التفضيلات (DPO).
• —Português: ~3,4M de exemplos nativos em turco para instruction-tuning, raciocínio (CoT) e alinhamento por preferências (DPO).
• —日本語: 指示チューニング・思考連鎖推論・DPO選好整合のためのネイティブトルコ語データ約340万件。
• —हिन्दी: इंस्ट्रक्शन-ट्यूनिंग, चेन-ऑफ-थॉट रीज़निंग और DPO प्रिफरेंस अलाइनमेंट के लिए ~3.4M मूल तुर्की उदाहरण।

📚 Composition

sft — chat format messages: [{role, content}]

dpo — {prompt, chosen, rejected} (~181k)

Turkish preference pairs for DPO / alignment.

🚀 Usage

from datasets import load_dataset

sft = load_dataset("erenfazlioglu/turkish-instruct-reasoning-dpo-3.4m", "sft", split="train")
dpo = load_dataset("erenfazlioglu/turkish-instruct-reasoning-dpo-3.4m", "dpo", split="train")

# verified reasoning only
verified = sft.filter(lambda x: x["verified"] and x["source"] in ("q2_reasoning", "eba_reasoning"))
# Turkey-grounded slice only
turkey = sft.filter(lambda x: x["source"] == "q1_turkey_grounded")

🛠️ How it was built

• —Synthetic generation: instructions and answers were generated/rewritten with large language models, scaled via batched generation.
• —Turkey-grounded slice (`q1`): built on real, public Turkish social-media / news text → teaches the model Turkish context, names and current affairs.
• —Verified reasoning: q2_reasoning and eba_reasoning outputs were checked by an independent second verification pass (flawed ones corrected or removed); math slices are matched against gold/source answers.
• —Cleaning: instruction-level dedup + empty/too-short/malformed filtering + provider-mention scrub.

⚠️ Limitations (honest)

• —Synthetic / distilled — outputs may contain errors or biases; only the *_reasoning and math_* slices are additionally verified. Other slices are not verified.
• —`up3` origin: instruction topics derive from English sets (Turkish text is native, but context is often non-Turkey). For Turkey context use q1 / eba.
• —`q1`: built on public posts; the author identity is not stored, but @mention handles may appear in text. Contains political / current-affairs content.
• —DPO rejected answers are deliberately lower quality (not human preference labels).

📝 License & attribution

Mixed provenance (license: other). Source instructions: InstrucTurca, OpenMathInstruct-2 (CC-BY-4.0), GSM8K (MIT), national education content (EBA), public social-media posts. Assistant outputs are synthetically generated. Released for research; review the terms of the underlying sources before use.

Curated by Eren Fazlıoğlu — X/Twitter: @erenfazlioglutr

📌 Citation

@misc{fazlioglu_2026_turkish_instruct,
  title  = {Turkish Instruct, Reasoning and DPO Dataset (~3.4M)},
  author = {Eren Fazlıoğlu},
  year   = {2026},
  url    = {https://huggingface.co/datasets/erenfazlioglu/turkish-instruct-reasoning-dpo-3.4m},
  note   = {X/Twitter: @erenfazlioglutr}
}

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