---
base_model: Qwen/Qwen2.5-7b-Instruct
library_name: transformers
license: cc-by-nc-4.0
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: limo_7b_noq
  results: []
pipeline_tag: text-generation
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# limo_7b_noq

This model is a fine-tuned version of Qwen2.5-7b-Instruct on the S1_QFFT dataset as described in the paper [QFFT, Question-Free Fine-Tuning for Adaptive Reasoning](https://huggingface.co/papers/2506.12860).

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 1
- total_train_batch_size: 8
- total_eval_batch_size: 32
- optimizer: Use 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: 0.1
- num_epochs: 6

### Training results



### Framework versions

- Transformers 4.51.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- 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},
}
```

Code: https://github.com/AI-NEXT/LLaMA-Factory