---
license: other
language:
- en
- zh
pipeline_tag: text-generation
tags:
- qwen
- qwen3
- math
- sft
- supervised-fine-tuning
- full-finetuning
- reasoning
base_model: Qwen/Qwen3-1.7B-Base
---
Qwen3-1.7B-SFT
Qwen3-1.7B-SFT is a supervised fine-tuned model based on **Qwen3-1.7B-Base**, trained on the **DeepMath-4B** dataset for mathematical reasoning and problem-solving.
This model is associated with the paper:
**Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe**
Paper link: https://arxiv.org/abs/2604.13016
## Model Description
This model is obtained by full-parameter supervised fine-tuning (SFT) from `Qwen3-1.7B-Base`.
The training is designed to improve the model's performance on math-focused instruction-following and reasoning tasks.
This model is intended in **Section 5.1. Off-Policy Distillation from Teacher Rollouts as Cold Start**.
### Key characteristics
- **Base model**: Qwen3-1.7B-Base
- **Training stage**: Supervised Fine-Tuning (SFT)
- **Finetuning type**: Full finetuning
- **Primary domain**: Mathematical reasoning
- **Thinking mode**: Disabled during training (`enable_thinking: false`)
- **Context length**: 20480 tokens
## Training Details
### Training configuration
- **Framework**: LLaMA-Factory
- **Stage**: `sft`
- **Finetuning type**: `full`
- **DeepSpeed config**: `ds_z2_config.json`
- **Kernel optimization**: `enable_liger_kernel: True`
- **Precision**: `bf16`
- **Gradient checkpointing**: enabled
- **Learning rate**: `1e-5`
- **Scheduler**: cosine
- **Warmup ratio**: `0.1`
- **Number of epochs**: `2.0`
- **Per-device train batch size**: `16`
- **Gradient accumulation steps**: `1`
- **Validation split**: `0.01`
- **Evaluation strategy**: every `100` steps
- **Save strategy**: every `100` steps
### Dataset
- **Training dataset**: `deep_math_4b`
## Training Hyperparameters
For reproducibility, the core configuration is summarized below:
```
model_name_or_path: ../model/Qwen3-1.7B-Base
trust_remote_code: true
stage: sft
do_train: true
finetuning_type: full
deepspeed: examples/deepspeed/ds_z2_config.json
enable_liger_kernel: true
dataset: deep_math_4b
template: qwen3
enable_thinking: false
cutoff_len: 20480
preprocessing_num_workers: 64
dataloader_num_workers: 32
output_dir: ../model/Qwen3-1.7B-Base-SFT-DeepMath-4B
logging_steps: 5
save_steps: 100
plot_loss: true
overwrite_output_dir: true
save_only_model: true
report_to: swanlab
per_device_train_batch_size: 16
gradient_accumulation_steps: 1
gradient_checkpointing: true
learning_rate: 1.0e-5
num_train_epochs: 2.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
val_size: 0.01
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 100
```
## Citation
If you use this model, please consider citing the related paper:
```
@article{li2026rethinking,
title={Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe},
author={Li, Yaxuan and Zuo, Yuxin and He, Bingxiang and Zhang, Jinqian and Xiao, Chaojun and Qian, Cheng and Yu, Tianyu and Gao, Huan-ang and Yang, Wenkai and Liu, Zhiyuan and Ding, Ning},
journal={arXiv preprint arXiv:2604.13016},
year={2026}
}
```
Paper: https://arxiv.org/abs/2604.13016
