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README.md
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README.md
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---
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<div align="center">
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[在线体验 (推理模型)](https://www.modelscope.cn/studios/gongjy/MiniMind-Reasoning) | [在线体验 (常规模型)](https://www.modelscope.cn/studios/gongjy/MiniMind) | [Bilibili介绍](https://www.bilibili.com/video/BV12dHPeqE72/?share_source=copy_web&vd_source=670c2504f88726f8cf4a21ef6147c0e8)
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[🔗🍓推理模型](https://www.modelscope.cn/studios/gongjy/MiniMind-Reasoning) | [🔗🤖常规模型](https://www.modelscope.cn/studios/gongjy/MiniMind) | [🔗🎞️视频介绍](https://www.bilibili.com/video/BV12dHPeqE72/?share_source=copy_web&vd_source=670c2504f88726f8cf4a21ef6147c0e8)
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<div align="center">
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@ -288,10 +286,6 @@ python train_full_sft.py
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> 执行监督微调，得到 `full_sft_*.pth` 作为指令微调的输出权重（其中`full`即为全参数微调）
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---
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<details style="color:rgb(128,128,128)">
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<summary>注：训练须知</summary>
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@ -301,6 +295,9 @@ python train_full_sft.py
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</details>
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---
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### 4.测试模型效果
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确保需要测试的模型`*.pth`文件位于`./out/`目录下。
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@ -479,7 +476,7 @@ quality（当然也还不算high，提升数据质量无止尽）。
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> [!NOTE]
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> 2025-02-05后，开源MiniMind最终训练所用的所有数据集，因此无需再自行预处理大规模数据集，避免重复性的数据处理工作。
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MiniMind训练数据集 ([ModelScope](https://www.modelscope.cn/datasets/gongjy/minimind-dataset/files) | [HuggingFace](https://huggingface.co/datasets/jingyaogong))
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MiniMind训练数据集 ([ModelScope](https://www.modelscope.cn/datasets/gongjy/minimind-dataset/files) | [HuggingFace](https://huggingface.co/datasets/jingyaogong/minimind_dataset/tree/main))
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> 无需全部clone，可单独下载所需的文件
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@ -608,15 +605,13 @@ MiniMind的整体结构一致，只是在RoPE计算、推理函数和FFN层的
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✨基于单卡NVIDIA 3090的`MiniMind-Zero`从0训练仅需`2小时` + `3块钱`，实现ChatBot效果！
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✨PS：若采用8卡4090训练，总用时甚至可以压缩到10分钟以内！（由于时间更短，花费同样3元左右，与单卡成本相当）
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✨以极低极低的门槛，实现人人可玩的大模型自由！这正是MiniMind系列的诞生初衷！
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✨仅价值`3块钱`成本的`MiniMind-Zero`并不是噱头！Chat测试：
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```textmate
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```text
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👶: 请介绍一下自己。
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🤖️: 作为人工智能，我没有实际的生活，也没有自我意识，所以没有自己的生活。我被设计成能够帮助用户解答问题、提供信息、进行对话等。我的设计和功能是由计算机科学、机器学习、人工智能技术和算法所构建的，我通过编程和算法实现。
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@ -652,7 +647,7 @@ python train_pretrain.py
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```
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> 训练后的模型权重文件默认每隔`100步`保存为: `pretrain_*.pth`（*
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为模型具体dimension，每次保存时新文件会覆盖旧文件）
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> 为模型具体dimension，每次保存时新文件会覆盖旧文件）
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### **2. 有监督微调(Supervised Fine-Tuning)**:
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```
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> 训练后的模型权重文件默认每隔`100步`保存为: `full_sft_*.pth`（*
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为模型具体dimension，每次保存时新文件会覆盖旧文件）
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> 为模型具体dimension，每次保存时新文件会覆盖旧文件）
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## Ⅲ 其它训练步骤
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DPO通过推导PPO奖励模型的显式解，把在线奖励模型换成离线数据，Ref模型输出可以提前保存。
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DPO性能几乎不变，只用跑 actor_model 和 ref_model 两个模型，大大节省显存开销和增加训练稳定性。
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> 注：RLHF训练步骤**并非必须**，此步骤难以提升模型“智力”而通常仅用于提升模型的“礼貌”，有利（符合偏好、减少有害内容）也有弊（样本收集昂贵、反馈偏差、多样性损失）。
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```bash
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torchrun --nproc_per_node 1 train_dpo.py
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# or
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```
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> 训练后的模型权重文件默认每隔`100步`保存为: `rlhf_*.pth`（*
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为模型具体dimension，每次保存时新文件会覆盖旧文件）
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> 为模型具体dimension，每次保存时新文件会覆盖旧文件）
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### **4. 知识蒸馏(Knowledge Distillation, KD)**
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python train_lora.py
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```
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> 训练后的模型权重文件默认每隔`100步`保存为: `lora_xxx_*.pth`（*
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为模型具体dimension，每次保存时新文件会覆盖旧文件）
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> 为模型具体dimension，每次保存时新文件会覆盖旧文件）
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非常多的人困惑，如何使模型学会自己私有领域的知识？如何准备数据集？如何迁移通用领域模型打造垂域模型？
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## Ⅴ 训练结果
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MiniMind2 模型训练损失走势（由于数据集在训练后又更新清洗多次，因此Loss仅供参考）
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| models | pretrain (length-512) | sft (length-512) |
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|-----------------|----------------------------------------------------|----------------------------------------------------|
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| MiniMind2-Small | <img src="./images/pre_512_loss.png" width="100%"> | <img src="./images/sft_512_loss.png" width="100%"> |
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@ -917,15 +907,13 @@ MiniMind2 模型训练损失走势（由于数据集在训练后又更新清洗
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### 训练完成-模型合集
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> 考虑到多人反应百度网盘速度慢，MiniMind2及以后全部使用ModelScope/HuggingFace托管。
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#### ① PyTorch原生模型
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#### PyTorch原生模型
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MiniMind2模型权重 ([ModelScope](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch) | [HuggingFace](https://huggingface.co/jingyaogong/MiniMind2-Pytorch))
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* [MiniMind2系列 (ModelScope)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)
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* [MiniMind-V1系列 (百度网盘)](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)
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MiniMind-V1模型权重 ([百度网盘](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666))
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<details style="color:rgb(128,128,128)">
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<summary>Torch文件命名对照</summary>
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</details>
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#### Transformers模型
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#### ② Transformers模型
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* MiniMind系列 ([ModelScope](https://www.modelscope.cn/profile/gongjy)
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| [HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5))
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MiniMind系列 ([ModelScope](https://www.modelscope.cn/collections/MiniMind-b72f4cfeb74b47)
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| [HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5))
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---
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🏃以下测试于2025-02-09完成，此日期后发布的新模型，无特殊需要时将不加入测试。
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[A] [MiniMind2 (0.1B)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)<br/>
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[B] [MiniMind2-MoE (0.15B)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)<br/>
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[C] [MiniMind2-Small (0.02B)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)<br/>
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31
README_en.md
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README_en.md
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[Online Demo (Inference Model)](https://www.modelscope.cn/studios/gongjy/MiniMind-Reasoning) | [Online Demo (Standard Model)](https://www.modelscope.cn/studios/gongjy/MiniMind) | [Bilibili Introduction](https://www.bilibili.com/video/BV12dHPeqE72/?share_source=copy_web&vd_source=670c2504f88726f8cf4a21ef6147c0e8)
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[🔗🍓Reason Model](https://www.modelscope.cn/studios/gongjy/MiniMind-Reasoning) | [🔗🤖Standard Model](https://www.modelscope.cn/studios/gongjy/MiniMind) | [🔗🎞️Video Introduction](https://www.bilibili.com/video/BV12dHPeqE72/?share_source=copy_web&vd_source=670c2504f88726f8cf4a21ef6147c0e8)
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<div align="center">
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<table>
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> represents full parameter fine-tuning).
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---
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<details style="color:rgb(128,128,128)">
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<summary>Note: Training Information</summary>
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</details>
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---
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### 4. Testing Model Performance
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Ensure that the model `*.pth` file you want to test is located in the `./out/` directory.
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MiniMind Training Datasets are available for download from:
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- [ModelScope](https://www.modelscope.cn/datasets/gongjy/minimind-dataset/files)
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- [HuggingFace](https://huggingface.co/datasets/jingyaogong)
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(You don’t need to clone everything, just download the necessary files).
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Dataset ([ModelScope](https://www.modelscope.cn/datasets/gongjy/minimind-dataset/files) | [HuggingFace](https://huggingface.co/datasets/jingyaogong/minimind_dataset/tree/main))
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> You don’t need to clone everything, just download the necessary files.
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Place the downloaded dataset files in the `./dataset/` directory (✨ required files are marked):
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> Considering that many people have reported slow speeds with Baidu Cloud, all MiniMind2 models and beyond will be
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> hosted on ModelScope/HuggingFace.
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#### Native PyTorch Models
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---
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* [MiniMind2 Series (ModelScope)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)
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#### ① Native PyTorch Models
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* [MiniMind-V1 Series (Baidu Cloud)](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)
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MiniMind2 model
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weights ([ModelScope](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch) | [HuggingFace](https://huggingface.co/jingyaogong/MiniMind2-Pytorch))
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MiniMind-V1 model weights ([Baidu Pan](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666))
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<details style="color:rgb(128,128,128)">
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<summary>Torch File Naming Reference</summary>
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| MiniMind2-MoE | 145M | `pretrain_640_moe.pth` | `full_sft_640_moe.pth` | `rlhf_640_moe.pth` | - | - |
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| MiniMind2 | 104M | `pretrain_768.pth` | `full_sft_768.pth` | `rlhf_768.pth` | `reason_768.pth` | `lora_xxx_768.pth` |
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| Model Name | params | pretrain_model | Single-turn Chat sft | Multi-turn Chat sft | rl_model |
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| Model Name | params | pretrain_model | Single-turn Dialogue SFT | Multi-turn Dialogue SFT | rl_model |
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|-------------------|--------|------------------------|------------------------------------|-----------------------------------|--------------|
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| minimind-v1-small | 26M | `pretrain_512.pth` | `single_chat/full_sft_512.pth` | `multi_chat/full_sft_512.pth` | `rl_512.pth` |
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| minimind-v1-moe | 4×26M | `pretrain_512_moe.pth` | `single_chat/full_sft_512_moe.pth` | `multi_chat/full_sft_512_moe.pth` | - |
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</details>
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#### Transformers Models
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#### ② Transformers Models
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MiniMind
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Series ([ModelScope](https://www.modelscope.cn/collections/MiniMind-b72f4cfeb74b47) | [HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5))
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* MiniMind
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Series ([ModelScope](https://www.modelscope.cn/profile/gongjy) | [HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5))
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