update readme
This commit is contained in:
gongjy
parent
ddb7e666a3
commit
be9b434379
43
README.md
43
README.md
@ -40,13 +40,11 @@
|
||||
---
|
||||
|
||||
|
||||
|
||||
|
||||
<div align="center">
|
||||
|
||||
|
||||
|
||||
[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)
|
||||
[🔗🍓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)
|
||||
|
||||
|
||||
<div align="center">
|
||||
@ -288,10 +286,6 @@ python train_full_sft.py
|
||||
|
||||
> 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）
|
||||
|
||||
|
||||
---
|
||||
|
||||
|
||||
<details style="color:rgb(128,128,128)">
|
||||
<summary>注：训练须知</summary>
|
||||
|
||||
@ -301,6 +295,9 @@ python train_full_sft.py
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
---
|
||||
|
||||
### 4.测试模型效果
|
||||
|
||||
确保需要测试的模型`*.pth`文件位于`./out/`目录下。
|
||||
@ -479,7 +476,7 @@ quality（当然也还不算high，提升数据质量无止尽）。
|
||||
> [!NOTE]
|
||||
> 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。
|
||||
|
||||
MiniMind训练数据集 ([ModelScope](https://www.modelscope.cn/datasets/gongjy/minimind-dataset/files) | [HuggingFace](https://huggingface.co/datasets/jingyaogong))
|
||||
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))
|
||||
|
||||
> 无需全部clone，可单独下载所需的文件
|
||||
|
||||
@ -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
|
||||
|
||||
✨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！
|
||||
|
||||
|
||||
✨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）
|
||||
|
||||
✨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！
|
||||
|
||||
✨仅价值`3块钱`成本的`MiniMind-Zero`并不是噱头！Chat测试：
|
||||
|
||||
|
||||
```textmate
|
||||
```text
|
||||
👶: 请介绍一下自己。
|
||||
🤖️: 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。
|
||||
|
||||
@ -652,7 +647,7 @@ python train_pretrain.py
|
||||
```
|
||||
|
||||
> 训练后的模型权重文件默认每隔`100步`保存为: `pretrain_*.pth`（*
|
||||
为模型具体dimension，每次保存时新文件会覆盖旧文件）
|
||||
> 为模型具体dimension，每次保存时新文件会覆盖旧文件）
|
||||
|
||||
### **2. 有监督微调(Supervised Fine-Tuning)**:
|
||||
|
||||
@ -671,7 +666,7 @@ python train_full_sft.py
|
||||
```
|
||||
|
||||
> 训练后的模型权重文件默认每隔`100步`保存为: `full_sft_*.pth`（*
|
||||
为模型具体dimension，每次保存时新文件会覆盖旧文件）
|
||||
> 为模型具体dimension，每次保存时新文件会覆盖旧文件）
|
||||
|
||||
## Ⅲ 其它训练步骤
|
||||
|
||||
@ -685,10 +680,8 @@ python train_full_sft.py
|
||||
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。
|
||||
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。
|
||||
|
||||
|
||||
> 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）。
|
||||
|
||||
|
||||
```bash
|
||||
torchrun --nproc_per_node 1 train_dpo.py
|
||||
# or
|
||||
@ -696,7 +689,7 @@ python train_dpo.py
|
||||
```
|
||||
|
||||
> 训练后的模型权重文件默认每隔`100步`保存为: `rlhf_*.pth`（*
|
||||
为模型具体dimension，每次保存时新文件会覆盖旧文件）
|
||||
> 为模型具体dimension，每次保存时新文件会覆盖旧文件）
|
||||
|
||||
### **4. 知识蒸馏(Knowledge Distillation, KD)**
|
||||
|
||||
@ -746,9 +739,8 @@ torchrun --nproc_per_node 1 train_lora.py
|
||||
python train_lora.py
|
||||
```
|
||||
|
||||
|
||||
> 训练后的模型权重文件默认每隔`100步`保存为: `lora_xxx_*.pth`（*
|
||||
为模型具体dimension，每次保存时新文件会覆盖旧文件）
|
||||
> 为模型具体dimension，每次保存时新文件会覆盖旧文件）
|
||||
|
||||
|
||||
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？
|
||||
@ -906,10 +898,8 @@ MobileLLM提出架构的深度比宽度更重要，「深而窄」的「瘦长
|
||||
|
||||
## Ⅴ 训练结果
|
||||
|
||||
|
||||
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）
|
||||
|
||||
|
||||
| models | pretrain (length-512) | sft (length-512) |
|
||||
|-----------------|----------------------------------------------------|----------------------------------------------------|
|
||||
| MiniMind2-Small | <img src="./images/pre_512_loss.png" width="100%"> | <img src="./images/sft_512_loss.png" width="100%"> |
|
||||
@ -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
|
||||
|
||||
### 训练完成-模型合集
|
||||
|
||||
|
||||
> 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。
|
||||
|
||||
#### ① PyTorch原生模型
|
||||
|
||||
#### PyTorch原生模型
|
||||
MiniMind2模型权重 ([ModelScope](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch) | [HuggingFace](https://huggingface.co/jingyaogong/MiniMind2-Pytorch))
|
||||
|
||||
* [MiniMind2系列 (ModelScope)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)
|
||||
|
||||
* [MiniMind-V1系列 (百度网盘)](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)
|
||||
MiniMind-V1模型权重 ([百度网盘](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666))
|
||||
|
||||
<details style="color:rgb(128,128,128)">
|
||||
<summary>Torch文件命名对照</summary>
|
||||
@ -944,9 +932,9 @@ MiniMind2 模型训练损失走势（由于数据集在训练后又更新清洗
|
||||
|
||||
</details>
|
||||
|
||||
#### Transformers模型
|
||||
#### ② Transformers模型
|
||||
|
||||
* MiniMind系列 ([ModelScope](https://www.modelscope.cn/profile/gongjy)
|
||||
MiniMind系列 ([ModelScope](https://www.modelscope.cn/collections/MiniMind-b72f4cfeb74b47)
|
||||
| [HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5))
|
||||
|
||||
---
|
||||
@ -996,7 +984,6 @@ DPO和在线PPO的区别在于reject和chosen都是离线准备的，和minimind
|
||||
|
||||
🏃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。
|
||||
|
||||
|
||||
[A] [MiniMind2 (0.1B)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)<br/>
|
||||
[B] [MiniMind2-MoE (0.15B)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)<br/>
|
||||
[C] [MiniMind2-Small (0.02B)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)<br/>
|
||||
|
||||
31
README_en.md
31
README_en.md
@ -50,8 +50,7 @@
|
||||
|
||||
|
||||
|
||||
[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)
|
||||
|
||||
[🔗🍓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)
|
||||
|
||||
<div align="center">
|
||||
<table>
|
||||
@ -307,8 +306,6 @@ python train_full_sft.py
|
||||
> represents full parameter fine-tuning).
|
||||
|
||||
|
||||
---
|
||||
|
||||
<details style="color:rgb(128,128,128)">
|
||||
<summary>Note: Training Information</summary>
|
||||
|
||||
@ -321,6 +318,8 @@ below.
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
### 4. Testing Model Performance
|
||||
|
||||
Ensure that the model `*.pth` file you want to test is located in the `./out/` directory.
|
||||
@ -517,9 +516,9 @@ Big respect!
|
||||
|
||||
MiniMind Training Datasets are available for download from:
|
||||
|
||||
- [ModelScope](https://www.modelscope.cn/datasets/gongjy/minimind-dataset/files)
|
||||
- [HuggingFace](https://huggingface.co/datasets/jingyaogong)
|
||||
(You don’t need to clone everything, just download the necessary files).
|
||||
Dataset ([ModelScope](https://www.modelscope.cn/datasets/gongjy/minimind-dataset/files) | [HuggingFace](https://huggingface.co/datasets/jingyaogong/minimind_dataset/tree/main))
|
||||
|
||||
> You don’t need to clone everything, just download the necessary files.
|
||||
|
||||
Place the downloaded dataset files in the `./dataset/` directory (✨ required files are marked):
|
||||
|
||||
@ -1026,11 +1025,14 @@ For reference, the parameter settings for GPT-3 are shown in the table below:
|
||||
> Considering that many people have reported slow speeds with Baidu Cloud, all MiniMind2 models and beyond will be
|
||||
> hosted on ModelScope/HuggingFace.
|
||||
|
||||
#### Native PyTorch Models
|
||||
---
|
||||
|
||||
* [MiniMind2 Series (ModelScope)](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch)
|
||||
#### ① Native PyTorch Models
|
||||
|
||||
* [MiniMind-V1 Series (Baidu Cloud)](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)
|
||||
MiniMind2 model
|
||||
weights ([ModelScope](https://www.modelscope.cn/models/gongjy/MiniMind2-PyTorch) | [HuggingFace](https://huggingface.co/jingyaogong/MiniMind2-Pytorch))
|
||||
|
||||
MiniMind-V1 model weights ([Baidu Pan](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666))
|
||||
|
||||
<details style="color:rgb(128,128,128)">
|
||||
<summary>Torch File Naming Reference</summary>
|
||||
@ -1041,7 +1043,7 @@ For reference, the parameter settings for GPT-3 are shown in the table below:
|
||||
| MiniMind2-MoE | 145M | `pretrain_640_moe.pth` | `full_sft_640_moe.pth` | `rlhf_640_moe.pth` | - | - |
|
||||
| MiniMind2 | 104M | `pretrain_768.pth` | `full_sft_768.pth` | `rlhf_768.pth` | `reason_768.pth` | `lora_xxx_768.pth` |
|
||||
|
||||
| Model Name | params | pretrain_model | Single-turn Chat sft | Multi-turn Chat sft | rl_model |
|
||||
| Model Name | params | pretrain_model | Single-turn Dialogue SFT | Multi-turn Dialogue SFT | rl_model |
|
||||
|-------------------|--------|------------------------|------------------------------------|-----------------------------------|--------------|
|
||||
| minimind-v1-small | 26M | `pretrain_512.pth` | `single_chat/full_sft_512.pth` | `multi_chat/full_sft_512.pth` | `rl_512.pth` |
|
||||
| minimind-v1-moe | 4×26M | `pretrain_512_moe.pth` | `single_chat/full_sft_512_moe.pth` | `multi_chat/full_sft_512_moe.pth` | - |
|
||||
@ -1049,10 +1051,11 @@ For reference, the parameter settings for GPT-3 are shown in the table below:
|
||||
|
||||
</details>
|
||||
|
||||
#### Transformers Models
|
||||
#### ② Transformers Models
|
||||
|
||||
MiniMind
|
||||
Series ([ModelScope](https://www.modelscope.cn/collections/MiniMind-b72f4cfeb74b47) | [HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5))
|
||||
|
||||
* MiniMind
|
||||
Series ([ModelScope](https://www.modelscope.cn/profile/gongjy) | [HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5))
|
||||
|
||||
---
|
||||
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 72 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 128 KiB |
BIN
images/logo.png
BIN
images/logo.png
Binary file not shown.
|
Before Width: | Height: | Size: 402 KiB After Width: | Height: | Size: 495 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 39 KiB |
Loading…
x
Reference in New Issue
Block a user