from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    "uisikdag/qwen3-14b-arc-tr",
    max_seq_length=2048,
    load_in_4bit=True,
)
tokenizer = get_chat_template(tokenizer, chat_template="qwen2.5")
FastLanguageModel.for_inference(model)

# Predict
question = "Fotosentez sırasında bitkiler hangi gazı üretir?"
options = ["A Karbondioksit", "B Oksijen", "C Azot", "D Hidrojen"]

prompt = f"Soru: {question}\n\nSecenekler:\n" + "\n".join(options) + "\n\nDogru cevap hangisi?"
messages = [{"role": "user", "content": prompt}]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids=inputs, max_new_tokens=64, temperature=0.1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True).split("assistant")[-1].strip()
print(f"Cevap: {response}")

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.24.0
Transformers: 4.57.1
Pytorch: 2.9.0+cu128
Datasets: 4.3.0
Tokenizers: 0.22.2

Citations

Cite TRL as:

bibtex

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}