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[update] readme

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jingyaogong 2026-02-05 00:23:20 +08:00
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README.md
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@ -212,12 +212,12 @@ print(torch.cuda.is_available())
Pretrain数据：
```bash
wget https://hf-mirror.com/datasets/jingyaogong/minimind-v_dataset/resolve/main/pretrain_data.parquet
wget https://hf-mirror.com/datasets/jingyaogong/minimind-v_dataset/resolve/main/pretrain_i2t.parquet
```
SFT数据：
```bash
wget https://hf-mirror.com/datasets/jingyaogong/minimind-v_dataset/resolve/main/sft_data.parquet
wget https://hf-mirror.com/datasets/jingyaogong/minimind-v_dataset/resolve/main/sft_i2t.parquet
```
建议预留~2GB空间存放数据集，若无多余空间存放pretrain数据，可尝试跳过pretrain训练步骤直接进行sft训练。

4
README_en.md
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@ -220,12 +220,12 @@ and place it under `./dataset`.
Pretrain data:
```bash
wget https://hf-mirror.com/datasets/jingyaogong/minimind-v_dataset/resolve/main/pretrain_data.parquet
wget https://hf-mirror.com/datasets/jingyaogong/minimind-v_dataset/resolve/main/pretrain_i2t.parquet
```
SFT data:
```bash
wget https://hf-mirror.com/datasets/jingyaogong/minimind-v_dataset/resolve/main/sft_data.parquet
wget https://hf-mirror.com/datasets/jingyaogong/minimind-v_dataset/resolve/main/sft_i2t.parquet
```
Please reserve about ~2GB of space for the dataset. If there is insufficient space for pretrain data, you can try skipping the pretrain training step and proceed directly to SFT training.