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[feat] update readme

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jingyaogong 2025-10-21 22:53:00 +08:00
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README.md
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@ -128,9 +128,9 @@ git clone https://www.modelscope.cn/models/openai-mirror/clip-vit-base-patch16
```bash
# 下载纯语言模型权重到 ./out 目录下（作为训练VLM的基座语言模型）
https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch/blob/main/lm_512.pth
https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch/blob/main/llm_512.pth
# or
https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch/blob/main/lm_768.pth
https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch/blob/main/llm_768.pth
```
## Ⅰ 测试已有模型效果
@ -347,7 +347,7 @@ GPT模型根据现有token预测输出下一个下下一个下下下一个token
下面，我们简单讨论MiniMind-V的外部输入输出的变化。
VLM的输入依然是一段文本，其中包含特殊的<image>占位符。
VLM的输入依然是一段文本，其中包含特殊的`<image>`占位符。
在计算文本嵌入后，可以将图像编码器生成的向量投影到该占位符对应的嵌入部分，替换掉原先的占位符embedding。
例如：

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README_en.md
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@ -134,9 +134,9 @@ git clone https://www.modelscope.cn/models/openai-mirror/clip-vit-base-patch16
```bash
# Download the pure language model to the ./out directory (as the base language model for training VLM):
https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch/blob/main/lm_512.pth
https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch/blob/main/llm_512.pth
# or
https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch/blob/main/lm_768.pth
https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch/blob/main/llm_768.pth
```
@ -377,7 +377,7 @@ With that, the internal structural changes of MiniMind-V are now fully presented
Next, let's briefly discuss the changes in the external input and output of MiniMind-V.
The input to the VLM is still a segment of text containing special <image> placeholders.
The input to the VLM is still a segment of text containing special `<image>` placeholders.
After computing the text embedding, the vector generated by the image encoder can be projected onto the corresponding
embedding part of the placeholder, replacing the original placeholder embedding.
For example: