Update README.md

README.md

@@ -3,6 +3,7 @@ library_name: transformers
 license: apache-2.0
 datasets:
 - monology/pile-uncopyrighted
+- MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5
 language:
 - en
 metrics:
@@ -16,6 +17,8 @@ pipeline_tag: text-generation
 
 **MiniPLM-QWen-200M** is a 200M model with QWen achitecture pre-trained from scratch on [the Pile](https://huggingface.co/datasets/monology/pile-uncopyrighted) using the MiniPLM knowledge distillation framework with the [offcial QWen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) as the teacher model.
 
+We also open-source the [pre-training corpus](https://huggingface.co/datasets/MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5) refined by Difference Sampling in MiniPLM for reproducibility.
+
 <p align='left'>
     <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/2BqT0NgkmIXYlktovw9kG.png" width="1000">
 </p>
@@ -29,7 +32,9 @@ MiniPLM models achieves better performance given the same computation and scales
 </p>
 
 ## Baseline Models
-+ [Conventional Pre-Training](https://huggingface.co/MiniLLM/Pretrain-Qwen-1.2B)
-+ [VanillaKD](https://huggingface.co/MiniLLM/VanillaKD-Pretrain-Qwen-1.2B)
++ [Conventional Pre-Training](https://huggingface.co/MiniLLM/Pretrain-Qwen-200M)
++ [VanillaKD](https://huggingface.co/MiniLLM/VanillaKD-Pretrain-Qwen-200M)
 
 ## Citation
+
+TODO