update readme

README.md

@@ -2,7 +2,7 @@
  * @Author: LiangSong(sl12160010@gmail.com)
  * @Date: 2023-03-10 21:18:35
  * @LastEditors: LiangSong(sl12160010@gmail.com)
- * @LastEditTime: 2023-03-27 02:40:54
+ * @LastEditTime: 2023-03-29 21:44:01
  * @FilePath: /Open-Llama/README.md
  * @Description: 
  * 
@@ -14,6 +14,15 @@
 
 Open-Llama是一个开源项目，提供了一整套用于构建大型语言模型的训练流程，从数据集准备到分词、预训练、指令调优，以及强化学习技术 RLHF。
 
+## 进展
+
+经过30K step的预训练，已经展现出了一定的胡说八道的能力，如下分别是编写代码、续写论文和续写中文，虽然正确性存在问题但是已经像是一句话了。
+<div style="display:flex;">
+  <div style="flex:1"><img src="assets/code.JPG" width="100%"></div>
+  <div style="flex:1"><img src="assets/paper.JPG" width="100%"></div>
+  <div style="flex:1"><img src="assets/chinese.JPG" width="100%"></div>
+</div>
+
 ## **特性**
 
 ### 易用性

README_en.md

@@ -2,7 +2,7 @@
  * @Author: LiangSong(sl12160010@gmail.com)
  * @Date: 2023-03-10 21:18:35
  * @LastEditors: LiangSong(sl12160010@gmail.com)
- * @LastEditTime: 2023-03-27 02:41:39
+ * @LastEditTime: 2023-03-29 21:44:43
  * @FilePath: /Open-Llama/README_en.md
  * @Description: 
  * 
@@ -14,6 +14,15 @@ Translated by ChatGPT.
 
 Open-Llama is an open source project that provides a complete set of training processes for building large-scale language models, from data preparation to tokenization, pre-training, instruction tuning, and reinforcement learning techniques such as RLHF.
 
+## Progress
+After 30K steps of pre-training, the model has demonstrated some language capabilities, as shown below in coding, paper continuation, and Chinese continuation tasks. Although there are still issues with correctness, the generated outputs resemble sentences.
+
+<div style="display:flex;">
+  <div style="flex:1"><img src="assets/code.JPG" width="100%"></div>
+  <div style="flex:1"><img src="assets/paper.JPG" width="100%"></div>
+  <div style="flex:1"><img src="assets/chinese.JPG" width="100%"></div>
+</div>
+
 ## **Features**
 ### Ease of Use
 We believe that ease of use is one of the most important features when building large-scale language models. To make Open-Llama more accessible, we focus on the following: