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README.md

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   机器并不会区分这些信号的模态来源，而只是处理和分析这些序列背后所承载的信息内容。
 
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 私以为，**G**enerative **P**retrained **T**ransformer (GPT) 比 **L**arge **L**anguage **M**odel (LLM)更为贴切，
 因此本人表达上更习惯用"GPT"去代表LLM/VLM/类GPT架构的系列模型，而非为了蹭OpenAI的热度。
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 至此，我们可以用一句话总结GPT的所作所为：
 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 ...，直到模型输出结束符；此处的"token"其实并不需要一定是文本！
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 * 对于LLM模型，如果需要理解"图片"，我们只要把"图片"作为对一种特殊的从来没见过的"外国语言"，通过"外语词典"翻译后即可作为特殊的语言输入LLM

README_en.md

@@ -207,11 +207,15 @@ At this point, it's interesting to ponder two questions: What is a **L**arge **L
   Machines do not differentiate the modality source of these signals but rather process and analyze the information content carried by these sequences.
 
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 I personally believe that **G**enerative **P**retrained **T**ransformer (GPT) is a more fitting term than **L**arge **L**anguage **M**odel (LLM),
 and thus I prefer to use "GPT" to represent LLM/VLM/GPT-like architectures, rather than to piggyback on OpenAI's popularity.
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 In summary, we can encapsulate what GPT does in one sentence:
 GPT models predict the next, and the next, and the next token... until the model outputs an end token; here, the "token" does not necessarily have to be text!
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 * For LLM models, if understanding "images" is required, we can treat "images" as a special kind of "foreign language" never seen before, translating them through a "foreign dictionary" into a special language input for the LLM.