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---
license: apache-2.0
language:
- tr
pipeline_tag: text-generation
library_name: transformers
base_model: vngrs-ai/Kumru-2B
datasets:
- dogukanvzr/kumru-v5-dataset
tags:
- turkish
- türkçe
- reasoning
- thinking
- chain-of-thought
- full-finetune
- fine-tuned
- text-generation
- conversational
---
<p align="center">
<img src="./assets/kumru_header.png" alt="Kumru-2B-Thinking-v2.0 header" width="100%">
</p>
<p align="center">
<img src="https://img.shields.io/badge/Language-Turkish-red?style=for-the-badge" />
<img src="https://img.shields.io/badge/Model-2.375B-blue?style=for-the-badge" />
<img src="https://img.shields.io/badge/Fine--tune-Full--FT-purple?style=for-the-badge" />
<img src="https://img.shields.io/badge/Context-8192-orange?style=for-the-badge" />
<img src="https://img.shields.io/badge/License-Apache--2.0-green?style=for-the-badge" />
</p>
<p align="center">
<b>A full-parameter Turkish reasoning model fine-tuned to generate structured <code>&lt;think&gt;...&lt;/think&gt;</code> traces.</b>
</p>
<p align="center">
<img src="./assets/model_specs_strip.png" alt="Model specs strip" width="100%">
</p>
# Kumru-2B-Thinking-v2.0
**Kumru-2B-Thinking-v2.0** is a Turkish reasoning-oriented language model built on top of **[`vngrs-ai/Kumru-2B`](https://huggingface.co/vngrs-ai/Kumru-2B)**.
It is the **full-parameter fine-tune** successor to **[`Kumru-2B-Thinking-v1.0`](https://huggingface.co/dogukanvzr/Kumru-2B-Thinking-v1.0)**. While v1.0 used LoRA r=128, v2.0 updates the full 2.375B-parameter backbone and keeps the same `<think>...</think>` reasoning interface.
This release is aimed at Turkish reasoning experiments, educational demos, benchmark studies, and further alignment / fine-tuning research. It is not meant to be a high-stakes expert system.
---
## Table of contents
- [Model summary](#model-summary)
- [Overview](#overview)
- [Türkçe açıklama](#türkçe-açıklama)
- [Quick start](#quick-start)
- [Recommended generation settings](#recommended-generation-settings)
- [Reasoning format](#reasoning-format)
- [Benchmarks](#benchmarks)
- [v2.0 vs v1.0](#v20-vs-v10)
- [Training recipe](#training-recipe)
- [Training data](#training-data)
- [Intended use](#intended-use)
- [Limitations](#limitations)
- [Citation](#citation)
- [Acknowledgments](#acknowledgments)
- [License](#license)
---
## Model summary
| Field | Value |
|---|---|
| Model name | `dogukanvzr/Kumru-2B-Thinking-v2.0` |
| Base model | `vngrs-ai/Kumru-2B` |
| Approx. size | 2.375B parameters |
| Fine-tuning method | Full-parameter fine-tuning |
| Main language | Turkish |
| Context length | 8192 tokens |
| Main output style | `<think>...</think>` + final answer |
| License | Apache-2.0 |
| Predecessor | `Kumru-2B-Thinking-v1.0` (LoRA) |
---
## Overview
Kumru-2B-Thinking-v2.0 is the **full fine-tune evolution** of Kumru-2B-Thinking-v1.0.
The goal of the model is to produce **structured and inspectable Turkish reasoning-style outputs** while preserving a practical chat interface. Compared with v1.0, this release expands the adaptation surface from LoRA layers to the entire backbone, which leads to richer reasoning traces and better performance on several commonsense and reading-comprehension style tasks.
When to choose v2.0:
- You want richer Turkish reasoning traces.
- You care more about HellaSwag-TR, Belebele-TR, ARC-TR, and TruthfulQA-TR style performance.
- You want a compact full-FT research release rather than a LoRA adaptation.
When v1.0 may still be preferable:
- You need stricter multiple-choice format adherence.
- You care more about MMLU-TR and Winogrande-TR style tasks.
- You want the narrower behavior of the LoRA-tuned version.
---
## Türkçe açıklama
**Kumru-2B-Thinking-v2.0**, Kumru-2B-Thinking-v1.0'ın **full-parameter fine-tune** devam sürümüdür.
v1.0 LoRA r=128 ile daha dar bir güncelleme yüzeyi kullanırken, v2.0 modelin tüm 2.375B parametresini günceller. Böylece aynı `<think>...</think>` arayüzünü korurken daha zengin reasoning izleri ve bazı görevlerde daha güçlü performans sunar.
Özellikle HellaSwag-TR, Belebele-TR, ARC-TR ve TruthfulQA-TR gibi görevlerde v2.0 daha güçlüdür. Buna karşılık, MMLU-TR ve Winogrande-TR gibi daha format-hassas çoktan seçmeli görevlerde v1.0 bazı durumlarda daha iyi kalabilir.
---
## Quick start
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "dogukanvzr/Kumru-2B-Thinking-v2.0"
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?
"
"Adım adım düşün. Sonucu son satırda 'FINAL_ANSWER: <sayı>' formatında yaz."
),
}
]
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=0.6,
do_sample=True,
top_p=0.9,
repetition_penalty=1.05,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
generated = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=False)
print(generated)
```
---
## Recommended generation settings
For reasoning-style interactive use:
```python
generation_config = {
"max_new_tokens": 1024,
"temperature": 0.6,
"top_p": 0.9,
"do_sample": True,
"repetition_penalty": 1.05,
}
```
For deterministic benchmark-style evaluation:
```python
generation_config = {
"max_new_tokens": 1024,
"do_sample": False,
}
```
Practical notes:
- Use Turkish prompts for best results.
- Put the full instruction in the `user` message; avoid relying on a system prompt.
- Use `max_new_tokens=1024` when you want the reasoning trace to complete.
- If you are compute-constrained, `768` can work, but expect some reasoning traces to be cut.
- 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
```python
import re
text = generated
match = re.search(r"<think>(.*?)</think>\s*(.*?)(?:<\|eot_id\|>|$)", text, re.DOTALL)
reasoning, answer = (match.group(1).strip(), match.group(2).strip()) if match else ("", text)
```
---
## Benchmarks
Setup:
```text
7 Turkish tasks × 250 samples × 3 seeds × 2 decoding strategies × max_new_tokens=1024
```
Metric:
```text
Accuracy (exact-match on final answer letter or final number)
```
Why 1024 tokens?
> The original v1.0 release reported numbers at 768 tokens, but a substantial portion of reasoning traces was being cut mid-thought. Both v1.0 and v2.0 were evaluated at 1024 tokens here for a fairer comparison.
<p align="center">
<img src="./assets/benchmark_average.png" alt="Average benchmark accuracy comparison" width="92%">
</p>
<p align="center">
<img src="./assets/benchmark_tasks_sampling.png" alt="Per-task benchmark results with sampling" width="92%">
</p>
### Greedy decoding
<p align="center">
<img src="./assets/benchmark_tasks_greedy.png" alt="Per-task benchmark results with greedy decoding" width="92%">
</p>
| 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 |
| **Kumru-2B-Thinking-v2.0** | **14.3** | **19.3** | **20.9** | **10.8** | **22.3** | **18.4** | **1.7** | **15.4** |
### 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 |
| **Kumru-2B-Thinking-v2.0** | **17.3** | **21.1** | **22.0** | **16.1** | **18.9** | **19.1** | **1.3** | **16.5** |
### Interpretation
v2.0 is **not** a strict replacement for v1.0. It is a broader full-parameter adaptation that wins on several tasks tied to commonsense and reading-comprehension style reasoning, while v1.0 remains stronger on some format-strict multiple-choice tasks.
---
## v2.0 vs v1.0
<p align="center">
<img src="./assets/v2_vs_v1_greedy_delta.png" alt="v2 vs v1 greedy delta" width="86%">
</p>
Key takeaways:
- **v2.0 wins** on HellaSwag-TR, Belebele-TR, TruthfulQA-TR, ARC-TR, and slightly on GSM8K-TR.
- **v1.0 wins** on MMLU-TR and Winogrande-TR.
- **Average greedy score:** 15.4% for v2.0 vs 14.5% for v1.0.
- **Average sampling score:** 16.5% for v2.0 vs 16.8% for v1.0.
---
## Training recipe
<p align="center">
<img src="./assets/training_pipeline.png" alt="Training pipeline" width="96%">
</p>
| Component | Value |
|---|---|
| Base model | `vngrs-ai/Kumru-2B` |
| Fine-tuning method | Full-parameter fine-tuning |
| Trainable parameters | All 2.375B parameters |
| Optimizer | `torchao.optim.AdamW8bit(bf16_stochastic_round=True)` |
| Learning rate | `2e-5`, cosine, warmup 5% |
| Effective batch size | 16 |
| Epochs | 1 |
| Max sequence length | 8192 |
| Precision | bfloat16 + stochastic rounding |
| Loss masking | Assistant-only |
| Hardware | 1 × RTX 5090 (32 GB VRAM) |
| Runtime | 2 h 19 m 44 s |
| Final train loss | 1.609 |
| Best eval loss | 1.6505 |
| Weight delta verification | 165 / 165 tracked parameter groups changed |
Key engineering decisions:
1. **No Unsloth full-FT path** was used for the final release.
2. **HF Trainer** was used directly for the full fine-tune.
3. **Stochastic rounding** was used to better preserve small RMSNorm updates.
4. A **post-training weight-change verification** step confirmed that the intended parameter groups were actually updated.
---
## Training data
The model was trained on the same **25.8K curated Turkish reasoning traces** used for v1.0.
Dataset:
```text
dogukanvzr/kumru-v5-dataset
```
The training format encourages the model to produce:
1. A Turkish reasoning section inside `<think>...</think>`
2. A concise final answer after the reasoning block
---
## Intended use
<p align="center">
<img src="./assets/positioning_cards.png" alt="Model positioning cards" width="96%">
</p>
This model is suitable for:
- Turkish reasoning experiments
- Educational assistant prototypes
- Benchmark and evaluation studies
- Chain-of-thought style output experiments
- Further full-FT / 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
- Fully automated expert systems
- High-stakes grading or evaluation
- Sensitive personal data processing
- Reliable math solving without external verification
- Production systems that require strong factual reliability
---
## 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, 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 overthink simple questions or produce longer reasoning than necessary. If you need shorter answers, reduce `max_new_tokens`.
### English and code-mixed prompts
The model is Turkish-first. It may respond to English or mixed 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:
1. Use Turkish prompts.
2. Put all task instructions in the `user` turn.
3. Ask for a strict final answer format when evaluating.
4. Use `max_new_tokens=1024` when the reasoning trace matters.
5. Verify math and factual claims externally.
6. Parse the final answer separately from the `<think>` block.
7. Prefer v2.0 for richer reasoning; prefer v1.0 for stricter MC formatting.
---
## Citation
```bibtex
@misc{veziroglu2026kumru2bthinkingv2,
title = {Kumru-2B-Thinking-v2.0: A Turkish Full-Fine-Tuned Reasoning Language Model},
author = {Dogukan Veziroglu},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/dogukanvzr/Kumru-2B-Thinking-v2.0}}
}
```
---
## Acknowledgments
This model is based on [`vngrs-ai/Kumru-2B`](https://huggingface.co/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 downstream applications.
---
## Contact
For questions, issues, or feedback, please use the Hugging Face community tab or contact the model author through the Hugging Face profile:
```text
dogukanvzr
```