From 3cd1ac1bb989f9714a412a75dbd8020758ae154a Mon Sep 17 00:00:00 2001
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Date: Fri, 12 Dec 2025 15:55:27 +0800
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diff --git a/README.md b/README.md
index 583486b..68ba465 100644
--- a/README.md
+++ b/README.md
@@ -1,95 +1,82 @@
-# 昇思MindSpore技术公开课
+
+
昇思MindSpore大模型专题课程
+
+
+
+### 课程特色
- ***探究前沿***：解读技术热点，解构热点模型
- ***应用实践***：理论实践相结合，手把手指导开发
- ***专家解读***：多领域专家，多元解读
- ***开源共享***：课程免费，课件代码开源
-- ***大赛赋能***：ICT大赛赋能课程（大模型专题第一、二期）
-- ***系列课程***：大模型专题课程开展中，其他专题课程敬请期待
-## 报名方式
+### 📢 最新消息
+
+- 2025-12-20 「课程更新」：新增DeepSeek系列模型解读（[查看详情](xxxx)）
+
+### 教研团队
-报名链接：https://xihe.mindspore.cn/course/foundation-model-v2/introduction
+
-
-  |
-  |
-  |
-
+
\ No newline at end of file
diff --git a/Season1.step_into_chatgpt/0.Course-Review/1-Transformer.md b/Season1.step_into_chatgpt/0.Course-Review/1-Transformer.md
deleted file mode 100644
index e506ff8..0000000
--- a/Season1.step_into_chatgpt/0.Course-Review/1-Transformer.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Transformer 课程回顾
-
-提到大型语言模型（LLM），我们不可避免地提到Transformer模型。以它为起点，自然语言处理（NLP）领域步入了新的篇章。
-
-Transformer使用纯自注意力机制替代了传统的RNN与CNN网络，极大地提升了模型在处理长文本和长距离依赖关系的效果和速度，为后续大型语言模型的发展奠定了基础。
-
-接下来我们对课程进行简单回顾，迎接下一节公开课的进一步深入。
-
-## 注意力机制
-
-- 注意力分数用来表示词元在序列中的重要性，分数越高，说明词元与任务的关联越强
-- scaled dot-product attention计算：$$\text{Attention}(Q, K) = \text{softmax}(\frac{QK^T}{\sqrt{d_{model}}})$$
-- 自注意力分数表示一个序列中，词元与词元之间的关系，query=key=value
-- 多头注意力从多方面捕捉输入内容特征，支持并行计算注意力分数
-
-## Transformer
-
-- Encoder-Decoder结构
-- Encoder负责抓取源序列的特征信息，并传递给Decoder，Decoder逐词输出翻译结果
-- 序列在输入前需通过位置编码添加位置信息，此处的编码信息是固定的，不会随模型优化而更新
-- EncoderLayer由多头注意力和前馈神经网络两个子层组成，中间进行残差连接与层归一化
-- DecoderLayer由两个多头注意力与一个前馈神经网络，共三个子层组成，中间进行残差连接与层归一化
-- DecoderLayer的多头子注意力需要额外添加掩码，表示它无法看到后面的词元
-
-## NLP中的数据预处理
-
-- 词典：收录输入中包含的词元，并将其映射为数字编码
-
-## MindSpore OOP+FP 混合编程
-
-
