docs: fix QLoRA paper link

training/README_en.md

@@ -43,7 +43,7 @@ Anima model is trained based on QLoRA's [33B guanaco](https://huggingface.co/tim
 We mainly use the Chinese training dataset put together by project [Chinese-Vicuna](https://github.com/Facico/Chinese-Vicuna):  [guanaco_belle_merge_v1.0](https://huggingface.co/datasets/Chinese-Vicuna/guanaco_belle_merge_v1.0) in our finetune training work.
 
 * **Rationale**：
-According to the conclusions in [QLoRA] (https://arxiv.org/abs/2305.14314)Appendix B.4 and Table 9's Grid Search: For QLoRA fine-tuning, a larger number of training samples is not necessarily better. 10,000 steps is a size with a relatively good ROI. Therefore, we want to choose a dataset with no less than 10,000 steps. The [Belle 10M](https://github.com/LianjiaTech/BELLE/blob/main/data/10M) dataset seems too big, and the data quality is unclear to us. Due to limited time, we will first choose guanaco_belle_merge_v1.0. Later, we will further test more datasets and the effects of data quality filtering in a more systematic way.
+According to the conclusions in [QLoRA](https://arxiv.org/abs/2305.14314) Appendix B.4 and Table 9's Grid Search: For QLoRA fine-tuning, a larger number of training samples is not necessarily better. 10,000 steps is a size with a relatively good ROI. Therefore, we want to choose a dataset with no less than 10,000 steps. The [Belle 10M](https://github.com/LianjiaTech/BELLE/blob/main/data/10M) dataset seems too big, and the data quality is unclear to us. Due to limited time, we will first choose guanaco_belle_merge_v1.0. Later, we will further test more datasets and the effects of data quality filtering in a more systematic way.
 
 * **Acknowledgement**：Thanks [Chinese-Vicuna](https://github.com/Facico/Chinese-Vicuna)、[Belle](https://github.com/LianjiaTech/BELLE)、[GuanacoDataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) for their contributions to all the open datasets。