Instructions to use RefinedNeuro/RN_TR_R2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RefinedNeuro/RN_TR_R2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RefinedNeuro/RN_TR_R2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RefinedNeuro/RN_TR_R2")
model = AutoModelForCausalLM.from_pretrained("RefinedNeuro/RN_TR_R2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RefinedNeuro/RN_TR_R2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RefinedNeuro/RN_TR_R2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RefinedNeuro/RN_TR_R2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RefinedNeuro/RN_TR_R2

SGLang

How to use RefinedNeuro/RN_TR_R2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RefinedNeuro/RN_TR_R2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RefinedNeuro/RN_TR_R2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RefinedNeuro/RN_TR_R2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RefinedNeuro/RN_TR_R2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use RefinedNeuro/RN_TR_R2 with Docker Model Runner:
```
docker model run hf.co/RefinedNeuro/RN_TR_R2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Overview

RefinedNeuro/RN_TR_R2 is a Turkish‐language reasoning model fine-tuned from ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1 using the GRPO method on distilled Q&A data of Qwen3 model. It excels at open-ended reasoning tasks in Turkish, particularly STEM, history, and cultural questions.

🌐 Available on:: https://ollama.com/RefinedNeuro/RN_TR_R2

Evaluation

Benchmark: RN_TR_R2_Benchmark_Results (Open-ended Turkish culture & reasoning questions)

Model Name	Score (%)	Description
RefinedNeuro/RN_TR_R2	82.4	Upgraded from RN_TR_R1 via GRPO on distilled Qwen3 data
Qwen3-8B	76.8	Off-the-shelf Qwen3-8B
RefinedNeuro/RN_TR_R1	64.8	Baseline Turkish-fine-tuned model
ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1	63.2	Turkish-adapted Llama-8B via direct preference optimization
Meta-Llama-3.1-8B-Instruct	63.2	Meta’s Llama-3.1-8B with instruct tuning

RN_TR_R2 achieves a 17.6 pp improvement over the RN_TR_R1 baseline.

Intended Use

Primary use cases: Question answering, step-by-step reasoning, educational tools in Turkish (math, physics, chemistry, biology, geometry, trigonometry, statistics, history, culture).
Not recommended: Generating creative fiction or tasks requiring memorized facts outside its training scope.
Known limitations: While no systematic failure modes are identified, RN_TR_R2 may hallucinate like all LLMs.

Training Data

Base model: ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1
Fine-tuning dataset: RefinedNeuro/Qwen3-Reasoning-Distill-Q-A-Dataset
- Domains: Mathematics, Physics, Chemistry, Biology, Geometry, Trigonometry, Statistics
- Grade levels: 6th–12th grade
- Prompt variations (e.g., long/detailed vs. short/easy; answer must be a single number)
- Size: 13 000 examples (~9 million tokens)
- License: Creative Commons

Training Configuration

Method: GRPO (Generalized Reinforcement-Preference Optimization) via unsloth
Hardware: 2× RTX 3090 GPUs for 1 week (~336 GPU-hours)
Batch size: 1
Context length: 4 096 tokens (max_seq_length=8 192 at inference)
Optimizer & scheduler: Cosine LR schedule, peak LR=5×10⁻⁶
Library: unsloth==2025.3.18

Usage Example

pip install unsloth==2025.3.18

from unsloth import FastLanguageModel
from transformers import TextStreamer

# Load the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="RefinedNeuro/RN_TR_R2",
    max_seq_length=8192,
    load_in_4bit=True,
)

FastLanguageModel.for_inference(model)

SYSTEM_PROMPT = """
Sana sorulan sorulara cevap verirken, adım adım düşün ve gerekçelendir.
Önce <think></think> tagları arasında adım adım düşün.
Sonra düşünme adımlarını özetle.
En son \\boxed{} içerisinde sorunun cevabını ver.
"""

def answer_question(question: str):
    messages = [
        {'role': 'system', 'content': SYSTEM_PROMPT},
        {'role': 'user',   'content': question},
    ]
    inputs = tokenizer.apply_chat_template(
        messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
    ).to("cuda")

    streamer = TextStreamer(tokenizer)
    print("-" * 60)
    _ = model.generate(
        input_ids=inputs,
        streamer=streamer,
        max_new_tokens=2048,
        temperature=0.6,
        top_p=0.1,
    )

# Examples
answer_question("Türkiye'nin başkenti neredir?")
answer_question("6 ve 2'nin çarpımı nedir?")

Sample Q&A

> **Q:** f(x) = 3x⁴ - 2x³ + 5x - 7 fonksiyonunun x = 1 noktasındaki türevini bulunuz.
>
> **A:** <think>

1. Adım: f(x) = 3x⁴ - 2x³ + 5x - 7 fonksiyonunun türevini bulmak için, önce f(x)'nin türevini bulmamız gerekmektedir.

2. Adım: Türevi bulmak için fonksiyonu parçalara ayırarak analiz edeceğiz.

f(x) = 3x⁴ - 2x³ + 5x - 7

3. Adım: Türevi bulmak için her terimi ayrı ayrı türevini buluyoruz.

- 3x⁴: Türevi 12x³'dür.
- -2x³: Türevi -6x²'dir.
- 5x: Türevi 5'dir.
- -7: Türevi 0'dır (çünkü sabit bir sayıdır).

4. Adım: Türevi bulmak için elde edilen türevleri birleştiriyoruz.

f'(x) = 12x³ - 6x² + 5

5. Adım: Şimdi x = 1 yerine koyarak f'(x)'nin türevini hesaplayacağız.

f'(1) = 12(1)³ - 6(1)² + 5

f'(1) = 12(1) - 6(1) + 5

f'(1) = 12 - 6 + 5

f'(1) = 11

6. Adım: x = 1 noktasındaki türevi 11 olarak bulduk.

\boxed{11}

</think>

\boxed{11}

Citation

If you use this model, please cite:

@misc{farsak2025rn_tr_r2,
  title        = {RefinedNeuro/RN\_TR\_R2: A Turkish Reasoning Model},
  author       = {Mehmet Can Farsak and Serhat Atayeter and RefinedNeuro},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/RefinedNeuro/RN_TR_R2}},
  license      = {Apache-2.0}
}

License

Apache 2.0 — the most permissive open-source license.

Authors

Authors: Mehmet Can Farsak, Serhat Atayeter, RefinedNeuro

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Safetensors

Model size

8B params

Tensor type

F16

Model tree for RefinedNeuro/RN_TR_R2

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

ytu-ce-cosmos/Turkish-Llama-8b-Instruct-v0.1

Finetuned

ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1

Finetuned

(14)

this model

Adapters

1 model

Quantizations

1 model

RefinedNeuro
/

RN_TR_R2

Model Overview

Evaluation

Intended Use

Training Data

Training Configuration

Usage Example

Sample Q&A

Citation

License

Authors

Model tree for RefinedNeuro/RN_TR_R2

Dataset used to train RefinedNeuro/RN_TR_R2

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