Instructions to use Chan-Y/TurkishReasoner-Gemma3-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chan-Y/TurkishReasoner-Gemma3-12B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-12b-it")
model = PeftModel.from_pretrained(base_model, "Chan-Y/TurkishReasoner-Gemma3-12B")

Transformers

How to use Chan-Y/TurkishReasoner-Gemma3-12B with Transformers:

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

pipe = pipeline("text-generation", model="Chan-Y/TurkishReasoner-Gemma3-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Chan-Y/TurkishReasoner-Gemma3-12B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Chan-Y/TurkishReasoner-Gemma3-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chan-Y/TurkishReasoner-Gemma3-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chan-Y/TurkishReasoner-Gemma3-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Chan-Y/TurkishReasoner-Gemma3-12B

SGLang

How to use Chan-Y/TurkishReasoner-Gemma3-12B 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 "Chan-Y/TurkishReasoner-Gemma3-12B" \
    --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": "Chan-Y/TurkishReasoner-Gemma3-12B",
		"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 "Chan-Y/TurkishReasoner-Gemma3-12B" \
        --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": "Chan-Y/TurkishReasoner-Gemma3-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Chan-Y/TurkishReasoner-Gemma3-12B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Chan-Y/TurkishReasoner-Gemma3-12B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Chan-Y/TurkishReasoner-Gemma3-12B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Chan-Y/TurkishReasoner-Gemma3-12B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Chan-Y/TurkishReasoner-Gemma3-12B",
    max_seq_length=2048,
)

Docker Model Runner
How to use Chan-Y/TurkishReasoner-Gemma3-12B with Docker Model Runner:
```
docker model run hf.co/Chan-Y/TurkishReasoner-Gemma3-12B
```

TurkishReasoner-Gemma3-12B

Model Description

TurkishReasoner-Gemma3-12B is a specialized reasoning model fine-tuned from Google's Gemma3-12B specifically for Turkish language reasoning tasks. This model excels at structured problem-solving with step-by-step reasoning capabilities, making it ideal for complex mathematical, logical, and analytical problems in Turkish.

Key Features

Built on Google's multimodal Gemma3-12B foundation
Fine-tuned specifically for Turkish reasoning using GRPO (Group Relative Policy Optimization)
Supports both text and image inputs for comprehensive reasoning tasks
Delivers structured, step-by-step reasoning with clear solution formatting
Maintains the base model's 128K token context window
Trained on high-quality Turkish reasoning datasets including GSM8K-tr

Technical Specifications

Base Model: Google/Gemma3-12B
Parameters: 12 billion
Input: Text and images (multimodal capabilities)
Hardware Requirements: ~20GB VRAM (NVIDIA RTX 6000 Ada or equivalent)
Training Infrastructure: NVIDIA Ada6000 GPU

Usage

This model is optimized for reasoning-intensive applications in Turkish, including:

Educational tools requiring detailed mathematical explanations
Research applications exploring complex problem-solving
Applications requiring structured reasoning with visual components
Turkish-language AI assistants with advanced reasoning capabilities

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel
import torch

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-12b-it")
model = PeftModel.from_pretrained(base_model, "Chan-Y/TurkishReasoner-Gemma3-12B").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-3-12b-it")

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
)

messages = [
    {"role": "system", "content": """Sen kullanıcıların isteklerine Türkçe cevap veren bir asistansın ve sana bir problem verildi.
Problem hakkında düşün ve çalışmanı göster.
Çalışmanı <start_working_out> ve <end_working_out> arasına yerleştir.
Sonra, çözümünü <SOLUTION> ve </SOLUTION> arasına yerleştir.
Lütfen SADECE Türkçe kullan."""},
    {"role": "user", "content": "121'in karekökü kaçtır?"},
]

response = pipe(messages, return_full_text=False)[0]["generated_text"]
print(response)

For more information or assistance with this model, please contact the developers:

Cihan Yalçın: https://www.linkedin.com/in/chanyalcin/
Şevval Nur Savcı: https://www.linkedin.com/in/%C5%9Fevval-nur-savc%C4%B1/

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Model tree for Chan-Y/TurkishReasoner-Gemma3-12B

Base model

google/gemma-3-12b-pt

Finetuned

unsloth/gemma-3-12b-it

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TurkishReasoner

Collection

Models that are trained on reasoning task in Turkish language. • 4 items • Updated Nov 3, 2025 • 1