update readme
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gongjy
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README.md
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README.md
@ -248,7 +248,7 @@ streamlit run fast_inference.py
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# and
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python 3-full_sft.py
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```
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> 假设你的设备有N (N＞1) 张显卡：
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* 单机N卡启动训练(DDP)
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@ -469,13 +469,13 @@ MobileLLM提出架构的深度比宽度更重要，「深而窄」的「瘦长
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### 训练完成的模型权重
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[百度网盘](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)
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[🔗百度网盘](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)
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| Model Name | params | Config | pretrain_model | single_sft_model | multi_sft_model | rl_model |
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|-------------------|--------|-----------------------------|----------------------------------------------------------------|----------------------------------------------------------------|----------------------------------------------------------------|----------------------------------------------------------------|
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| minimind-v1-small | 26M | d_model=512<br/>n_layers=8 | [链接](https://pan.baidu.com/s/1wP_cAIc8cgaJ6CxUmR9ECQ?pwd=6666) | [链接](https://pan.baidu.com/s/1_COe0FQRDmeapSsvArahCA?pwd=6666) | [链接](https://pan.baidu.com/s/1GsGsWSL0Dckl0YPRXiBIFQ?pwd=6666) | [链接](https://pan.baidu.com/s/1C_dOCzNxr_XF3Qk3pkdrwg?pwd=6666) |
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| minimind-v1-moe | 4×26M | d_model=512<br/>n_layers=8 | [链接](https://pan.baidu.com/s/1IZdkzPRhbZ_bSsRL8vInjg?pwd=6666) | [链接](https://pan.baidu.com/s/1tqB-GMvuiGQBvEl-yZ-oBw?pwd=6666) | [链接](https://pan.baidu.com/s/1GHJ2T4904EcT1u8l1rVqtg?pwd=6666) | - |
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| minimind-v1 | 108M | d_model=768<br/>n_layers=16 | [链接](https://pan.baidu.com/s/1B60jYo4T8OmJI0ooqsixaA?pwd=6666) | [链接](https://pan.baidu.com/s/1p713loS7EfwHQf3G9eYI3Q?pwd=6666) | [链接](https://pan.baidu.com/s/12iHGpAs6R0kqsOnGtgK6vQ?pwd=6666) | [链接](https://pan.baidu.com/s/1vmUrir-UuucqBftqNPI4ng?pwd=6666) |
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| Model Name | params | Config | pretrain_model | single_sft_model | multi_sft_model | rl_model |
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|-------------------|--------|-----------------------------|------------------------|------------------------------------|-----------------------------------|--------------|
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| minimind-v1-small | 26M | d_model=512<br/>n_layers=8 | `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 | d_model=512<br/>n_layers=8 | `pretrain_512_moe.pth` | `single_chat/full_sft_512_moe.pth` | `multi_chat/full_sft_512_moe.pth` | - |
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| minimind-v1 | 108M | d_model=768<br/>n_layers=16 | `pretrain_768.pth` | `single_chat/full_sft_768.pth` | `multi_chat/full_sft_768.pth` | `rl_768.pth` |
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---
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@ -486,7 +486,8 @@ MobileLLM提出架构的深度比宽度更重要，「深而窄」的「瘦长
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> [!TIP]
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> 测试基于「单轮对话full_sft」和「DPO强化学习对齐」的minimind模型对比。
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模型文件[百度网盘](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)，其中 `rl_<dim>.pth` 即为「DPO强化学习对齐」后的minimind模型权重。
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模型文件[百度网盘](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)，其中 `rl_<dim>.pth`
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即为「DPO强化学习对齐」后的minimind模型权重。
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```text
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[Q]: 你叫什么名字？
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@ -515,6 +516,7 @@ MobileLLM提出架构的深度比宽度更重要，「深而窄」的「瘦长
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```
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### 👉效果总结
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* RLHF数据使用大约10万条；full_sft模型在简洁性和信息准确性方面表现更好；rl模型在回答中提供了更多的背景信息，但信息准确性有待改进。
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* 总的来说RLHF后的模型倾向于学习：说更多有礼貌但无用的废话讨好“对话”本身，而对信息准确性则有轻微损失。
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* 天下没有免费的午餐，还需要继续提升RLHF数据集的质量，也要接受模型能力无法避免的损失(程度有轻重)。
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12
README_en.md
12
README_en.md
@ -531,13 +531,13 @@ better with the scaling law for small models.
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### Trained Model Weights
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[baidu](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)
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[🔗Baidu Netdisk](https://pan.baidu.com/s/1KUfSzEkSXYbCCBj0Pw-9fA?pwd=6666)
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| Model Name | params | Config | pretrain_model | single_sft_model | multi_sft_model | rl_model |
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|-------------------|--------|-----------------------------|-----------------------------------------------------------------|-----------------------------------------------------------------|-----------------------------------------------------------------|----------|
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| minimind-v1-small | 26M | d_model=512<br/>n_layers=8 | [URL](https://pan.baidu.com/s/1wP_cAIc8cgaJ6CxUmR9ECQ?pwd=6666) | [URL](https://pan.baidu.com/s/1_COe0FQRDmeapSsvArahCA?pwd=6666) | [URL](https://pan.baidu.com/s/1GsGsWSL0Dckl0YPRXiBIFQ?pwd=6666) | | [URL](https://pan.baidu.com/s/1C_dOCzNxr_XF3Qk3pkdrwg?pwd=6666) |
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| minimind-v1-moe | 4×26M | d_model=512<br/>n_layers=8 | [URL](https://pan.baidu.com/s/1IZdkzPRhbZ_bSsRL8vInjg?pwd=6666) | [URL](https://pan.baidu.com/s/1tqB-GMvuiGQBvEl-yZ-oBw?pwd=6666) | [URL](https://pan.baidu.com/s/1GHJ2T4904EcT1u8l1rVqtg?pwd=6666) | | - |
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| minimind-v1 | 108M | d_model=768<br/>n_layers=16 | [URL](https://pan.baidu.com/s/1B60jYo4T8OmJI0ooqsixaA?pwd=6666) | [URL](https://pan.baidu.com/s/1p713loS7EfwHQf3G9eYI3Q?pwd=6666) | [URL](https://pan.baidu.com/s/12iHGpAs6R0kqsOnGtgK6vQ?pwd=6666) | | [URL](https://pan.baidu.com/s/1vmUrir-UuucqBftqNPI4ng?pwd=6666) |
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| Model Name | params | Config | pretrain_model | single_sft_model | multi_sft_model | rl_model |
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|-------------------|--------|-----------------------------|------------------------|------------------------------------|-----------------------------------|--------------|
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| minimind-v1-small | 26M | d_model=512<br/>n_layers=8 | `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 | d_model=512<br/>n_layers=8 | `pretrain_512_moe.pth` | `single_chat/full_sft_512_moe.pth` | `multi_chat/full_sft_512_moe.pth` | - |
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| minimind-v1 | 108M | d_model=768<br/>n_layers=16 | `pretrain_768.pth` | `single_chat/full_sft_768.pth` | `multi_chat/full_sft_768.pth` | `rl_768.pth` |
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---
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