diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..359bb53
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,3 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
diff --git a/.idea/MarsCodeWorkspaceAppSettings.xml b/.idea/MarsCodeWorkspaceAppSettings.xml
new file mode 100644
index 0000000..e2a065b
--- /dev/null
+++ b/.idea/MarsCodeWorkspaceAppSettings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml
new file mode 100644
index 0000000..ac21435
--- /dev/null
+++ b/.idea/inspectionProfiles/Project_Default.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/mini-vllm.iml b/.idea/mini-vllm.iml
new file mode 100644
index 0000000..64335a1
--- /dev/null
+++ b/.idea/mini-vllm.iml
@@ -0,0 +1,12 @@
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..e491a25
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..585642a
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/README.md b/README.md
index 2316c33..53706a8 100644
--- a/README.md
+++ b/README.md
@@ -31,6 +31,7 @@
+* 目前这是我用来入门大模型的项目
* 此开源项目旨在完全从0开始,仅用3块钱成本 + 2小时!即可训练出仅为25.8M的超小语言模型**MiniMind**。
* **MiniMind**系列极其轻量,最小版本体积是 GPT-3 的 $\frac{1}{7000}$,力求做到最普通的个人GPU也可快速训练。
* 项目同时开源了大模型的极简结构-包含拓展共享混合专家(MoE)、数据集清洗、预训练(Pretrain)、监督微调(SFT)、LoRA微调、直接偏好优化(DPO)、强化学习训练(RLAIF: PPO/GRPO等)、模型蒸馏等全过程代码。