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。