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dogukanvzr/
Kumru-2B-Thinking-v1.0

Name: Kumru-2B-Thinking-v1.0
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A compact Turkish reasoning model fine-tuned to generate structured <think>...</think> reasoning traces.

Kumru-2B-Thinking-v1.0

Kumru-2B-Thinking-v1.0 is a Turkish reasoning-oriented language model built on top of vngrs-ai/Kumru-2B.

It was fine-tuned with rank-128 LoRA on a curated Turkish reasoning dataset to make the model produce structured reasoning traces before giving its final answer.

The model usually answers in this format:

text

<think>
Türkçe akıl yürütme süreci burada yer alır.
</think>

Final cevap burada yer alır.

This release is intended for Turkish reasoning experiments, educational demos, benchmark studies, and further alignment or fine-tuning research. It is a compact 2.3B model, so it should be used with realistic expectations and external verification for tasks that require high reliability.

Model summary
Overview
Türkçe açıklama
Quick start
Recommended generation settings
Reasoning format
Benchmarks
Training recipe
Training data
Example output
Intended use
Limitations
Citation
Acknowledgments
License

Model summary

Field	Value
Model name	`dogukanvzr/Kumru-2B-Thinking-v1.0`
Base model	`vngrs-ai/Kumru-2B`
Approx. size	2.3B parameters
Fine-tuning method	LoRA
LoRA rank	128
Main language	Turkish
Context length	8192 tokens
Main output style	`<think>...</think>` + final answer
License	Apache-2.0

Overview

Kumru-2B-Thinking-v1.0 is designed to make a small Turkish language model better at producing structured and inspectable reasoning-style outputs.

The goal is not only to provide a short answer, but also to separate the model’s reasoning trace from the final answer. This makes the model useful for experiments where the reasoning process needs to be evaluated, parsed, compared, or used as synthetic training data.

The model is especially useful for:

Turkish reasoning-format experiments
Educational question answering demos
Turkish benchmark evaluation pipelines
Chain-of-thought style data generation experiments
Further LoRA/full fine-tuning experiments
GRPO/RL-style research starting from a Turkish reasoning model
Small-model reasoning behavior analysis

Compared with the original Kumru-2B-Instruct baseline, this model shows better benchmark performance under the evaluation setup used in this release and more consistently follows a Turkish thinking-style response format.

Türkçe açıklama

Kumru-2B-Thinking-v1.0, Türkçe akıl yürütme formatı üretmesi için fine-tune edilmiş kompakt bir dil modelidir.

Model, cevap vermeden önce genellikle <think>...</think> bloğu içinde Türkçe bir düşünme/akıl yürütme bölümü üretir. Ardından final cevabı ayrı şekilde verir. Bu yapı, özellikle Türkçe reasoning çalışmaları, eğitim amaçlı demolar, benchmark denemeleri ve daha sonraki fine-tuning/RL çalışmalarında kullanışlıdır.

Bu modelin amacı, Türkçe görevlerde daha okunabilir ve ayrıştırılabilir reasoning çıktıları üretmektir. Model küçük ölçekli olduğu için matematik, kodlama, güncel bilgi, uzun bağlamlı karmaşık çıkarım ve yüksek riskli karar verme görevlerinde tek başına güvenilir bir sistem olarak kullanılmamalıdır.

Quick start

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "dogukanvzr/Kumru-2B-Thinking-v1.0"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Recommended for faster generation during inference
model.config.use_cache = True

messages = [
    {
        "role": "user",
        "content": (
            "Bir trende 96 yolcu var. İlk durakta yolcuların dörtte biri iniyor "
            "ve 18 yolcu biniyor. İkinci durakta mevcut yolcuların üçte biri iniyor. "
            "Son durumda trende kaç yolcu kalır?"
        ),
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=1024,
    temperature=0.7,
    do_sample=True,
    top_p=0.95,
    repetition_penalty=1.05,
)

generated = tokenizer.decode(
    outputs[0][inputs.shape[-1]:],
    skip_special_tokens=True,
)

print(generated)

Recommended generation settings

For reasoning-style interactive use:

python

generation_config = {
    "max_new_tokens": 1024,
    "temperature": 0.6,
    "top_p": 0.95,
    "do_sample": True,
    "repetition_penalty": 1.05,
}

For more deterministic benchmark-style evaluation:

python

generation_config = {
    "max_new_tokens": 1024,
    "do_sample": False,
}

Practical notes:

Use Turkish prompts for best results.
Use max_new_tokens=768 or 1024 for reasoning-heavy tasks.
Use shorter token limits for simple Q&A if you do not need long reasoning.
For multiple-choice evaluation, ask the model to produce a strict final answer format.
If generation is slow, make sure model.config.use_cache = True is enabled.

Reasoning format

The model was trained to produce a visible reasoning block followed by the final answer:

text

<think>
Burada model problemi Türkçe olarak adım adım değerlendirir.
</think>

Burada final cevap yer alır.

For tasks where you need reliable extraction, explicitly request a final answer marker.

Example for multiple-choice tasks:

text

Aşağıdaki soruyu çöz. Seçenekleri dikkatlice değerlendir.
Cevabı düşünüp son satırda yalnızca şu formatta yaz:

FINAL_ANSWER: <A/B/C/D>

Example for numerical tasks:

text

Problemi adım adım çöz.
Son satırda yalnızca şu formatta yaz:

FINAL_ANSWER: <sayı>

Splitting reasoning and final answer

A simple parser can separate the <think> block from the final answer:

python

import re

def split_thinking(text: str):
    match = re.search(r"<think>(.*?)</think>", text, flags=re.DOTALL)

    if not match:
        return None, text.strip()

    reasoning = match.group(1).strip()
    final_answer = text[match.end():].strip()

    return reasoning, final_answer

Benchmarks

The model was evaluated on 7 Turkish benchmark tasks:

MMLU-TR
ARC-TR
TruthfulQA-TR
Winogrande-TR
HellaSwag-TR
Belebele-TR
GSM8K-TR

Evaluation setup:

7 tasks × 250 samples × 3 seeds × 2 decoding strategies

Reported metric:

Accuracy

For multiple-choice tasks, the answer was extracted from the final answer letter.
For GSM8K-TR, the final numerical answer was extracted.

Per-task benchmark results with sampling

Results with `max_new_tokens=1024`

max_new_tokens=1024 is recommended for this model because reasoning traces can be longer than normal instruction-tuned outputs.

Sampling, temperature 0.7

Model	MMLU-TR	ARC-TR	TruthQA-TR	Wino-TR	HSwag-TR	Belebele-TR	GSM8K-TR	Avg
Kumru-2B-Instruct	8.6	4.7	23.9	18.8	6.8	8.0	1.1	10.3
Kara-Kumru-v1.0	10.7	8.9	27.9	28.1	7.9	6.5	2.9	13.3
Kumru-2B-Thinking-v1.0	18.4	19.7	20.9	19.3	20.7	16.5	1.9	16.8

Greedy decoding

Model	MMLU-TR	ARC-TR	TruthQA-TR	Wino-TR	HSwag-TR	Belebele-TR	GSM8K-TR	Avg
Kumru-2B-Instruct	6.7	5.1	22.0	7.6	4.3	6.7	2.3	7.8
Kara-Kumru-v1.0	9.9	7.2	24.7	25.7	7.1	3.5	2.1	11.5
Kumru-2B-Thinking-v1.0	19.3	17.1	18.1	13.3	17.9	14.8	1.3	14.5

Interpretation

Kumru-2B-Thinking-v1.0 improves over the Kumru-2B-Instruct baseline in the tested setup, especially on several Turkish multiple-choice reasoning benchmarks.

The main value of the model is not only the benchmark improvement, but also its ability to produce a consistent, inspectable Turkish reasoning format.

At the same time, this is still a compact 2.3B model. Absolute scores remain limited, especially for mathematical reasoning. For math, factual questions, or high-reliability use cases, answers should be verified externally.

Additional token-budget visualizations

Training recipe

Kumru-2B-Thinking-v1.0 was fine-tuned from vngrs-ai/Kumru-2B using LoRA.

Component	Value
Base model	`vngrs-ai/Kumru-2B`
Fine-tuning method	LoRA
LoRA rank	128
LoRA alpha	128
LoRA variant	rsLoRA
Max sequence length	8192
Precision	bf16
Loss masking	Assistant-only
Main target language	Turkish
Main output format	`<think>...</think>` + final answer

The fine-tuning objective was to teach the model to generate structured Turkish reasoning traces while preserving general instruction-following behavior from the base model.

Training data

The model was trained on a curated Turkish reasoning dataset.

Dataset used:

dogukanvzr/kumru-v5-dataset

The dataset format encourages the model to produce:

A Turkish reasoning section inside <think>...</think>
A concise final answer after the reasoning block

The training data focuses on Turkish reasoning, instruction following, and answer formatting. The model is therefore expected to perform best on Turkish prompts.

Example output

Prompt:

Bir trende 96 yolcu var. İlk durakta yolcuların dörtte biri iniyor ve 18 yolcu biniyor. İkinci durakta mevcut yolcuların üçte biri iniyor. Şimdi trende kaç yolcu vardır?

Example style of output:

text

<think>
Başlangıçta trende 96 yolcu var.

İlk durakta yolcuların dörtte biri iner:
96 / 4 = 24

Bu durumda kalan yolcu sayısı:
96 - 24 = 72

Sonra 18 yolcu biner:
72 + 18 = 90

İkinci durakta mevcut yolcuların üçte biri iner:
90 / 3 = 30

Kalan yolcu sayısı:
90 - 30 = 60
</think>

Trende son durumda 60 yolcu vardır.

Intended use

This model is suitable for:

Turkish reasoning experiments
Turkish educational assistant prototypes
Chain-of-thought style output experiments
Turkish benchmark and evaluation pipelines
Synthetic reasoning data experiments
Further fine-tuning and alignment research
Small-model reasoning behavior analysis

Out-of-scope use

This model should not be used as the sole source of truth for:

Medical, legal, financial, or safety-critical decisions
High-stakes educational grading
Fully automated expert systems
Sensitive personal data processing
Production systems that require strong factual reliability
Mathematical solving without external verification
Code generation without testing

Limitations

Kumru-2B-Thinking-v1.0 has several known limitations.

Mathematical reasoning

The model can produce step-by-step reasoning, but numerical accuracy is not guaranteed. Mathematical answers should be checked with a calculator, symbolic tool, or a stronger verifier model.

Factual accuracy

The model may hallucinate facts, dates, names, sources, or explanations. It does not have live internet access and should not be treated as a current knowledge system.

Long reasoning traces

The model may sometimes overthink simple questions or produce longer reasoning than necessary. Use a lower max_new_tokens value for short responses and higher values for benchmark-style reasoning.

English and code-mixed prompts

The model is Turkish-first. It may respond to English or mixed Turkish-English prompts, but quality is expected to be better on Turkish prompts.

Formatting

The model is trained to use <think>...</think>, but downstream applications should still validate the output format if strict parsing is required.

Safety

The model has not been extensively safety-aligned beyond the behavior inherited from the base model and the fine-tuning data. Additional safety filtering is recommended for public-facing applications.

Practical recommendations

For best results:

Use Turkish prompts.
Ask for a strict final answer format when evaluating.
Use max_new_tokens=768 or 1024 for reasoning-heavy tasks.
Use temperature=0.6–0.7 for reasoning-style sampling.
Use greedy decoding for deterministic benchmark runs.
Verify math and factual claims externally.
Parse the final answer separately from the <think> block.
Avoid using the model as the only decision-maker in high-stakes settings.

Architecture

This model inherits the architecture and tokenizer from vngrs-ai/Kumru-2B.

Fine-tuning was performed with LoRA adapters applied to the main attention and MLP projection layers, with additional trainable components where needed for reasoning-format adaptation.

The released checkpoint is provided in safetensors format for loading with the Hugging Face Transformers ecosystem.

Chat template

The model uses a chat template compatible with Hugging Face apply_chat_template.

Recommended usage:

python

messages = [
    {"role": "user", "content": "Sorunu Türkçe olarak buraya yaz."}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
)

The model was trained primarily for direct Turkish user instructions. For best results, place task instructions clearly in the user message.

Loading with lower precision

Depending on hardware, the model can be loaded in bf16 or fp16.

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "dogukanvzr/Kumru-2B-Thinking-v1.0"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

model.config.use_cache = True

For limited VRAM environments, users may apply external quantization workflows such as bitsandbytes or GGUF conversion. Quantized releases are not guaranteed to match the benchmark results shown above unless evaluated separately.

Citation

If you use this model, please cite it as:

bibtex

@misc{veziroglu2026kumru2bthinkingv1,
  title        = {Kumru-2B-Thinking-v1.0: A Turkish Reasoning-Oriented Fine-Tuned Language Model},
  author       = {Dogukan Veziroglu},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/dogukanvzr/Kumru-2B-Thinking-v1.0}}
}

Acknowledgments

This model is based on vngrs-ai/Kumru-2B.

Thanks to the VNGRS-AI team for releasing Turkish language models that make further Turkish NLP and reasoning experiments possible.

License

This model is released under the Apache-2.0 license.

Please also check the license and usage terms of the base model and any datasets used in your downstream application.

Contact

For questions, issues, or feedback, please use the Hugging Face community tab or contact the model author through the Hugging Face profile:

dogukanvzr

dogukanvzr/Kumru-2B-Thinking-v1.0

Kumru-2B-Thinking-v1.0

Table of contents

Model summary

Overview

Türkçe açıklama

Quick start

Recommended generation settings

Reasoning format

Splitting reasoning and final answer

Benchmarks

Results with max_new_tokens=1024

Sampling, temperature 0.7

Greedy decoding

Interpretation

Training recipe

Training data

Example output

Intended use

Out-of-scope use

Limitations

Mathematical reasoning

Factual accuracy

Long reasoning traces

English and code-mixed prompts

Formatting

Safety

Practical recommendations

Architecture

Chat template

Loading with lower precision

Citation

Acknowledgments

License

Contact

dogukanvzr/
Kumru-2B-Thinking-v1.0

Results with `max_new_tokens=1024`