s1_32b_noq

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 1e-05
• train_batch_size: 1
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• total_eval_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 20

Training results

Framework versions

• Transformers 4.48.2
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0

📖 Citation

@misc{liu2025qfft,
  title={QFFT, Question-Free Fine-Tuning for Adaptive Reasoning},
  author={Wanlong Liu and Junxiao Xu and Fei Yu and Yukang Lin and Ke Ji and Wenyu Chen and Yan Xu and Yasheng Wang and Lifeng Shang and Benyou Wang},
  year={2025},
  eprint={2506.12860},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2506.12860},
}