北京时间昨夜Meta发布史上最强开源模型 Llama3，各种霸榜，号称GPT4级别体验

按照 简单三步跑谷歌Gemma-7B 中的步骤，第一时间可用上 Llama3 8B，自己亲自体验一下吧

ollama pull llama3     // 4.7G  不算大

ollama run llama3

中文支持有待提高，复杂一点的问题直接飙英文了:

>>> 你觉得范畴学和大模型transformer有什么联系吗

?    

范畴学（Category Theory）和 Transformer 模型之间确实存在一些关联。

**共同点：**

1. **Compositionality**: Both Category Theory and the Transformer model emphasize compositionality, which means breaking down complex structures into smaller, manageable components that can be composed together to form more complex entities.

        * In Category Theory, this is achieved through the concept of morphisms (functions) and functors (transformations

between categories).

        * In the Transformer model, it's done by representing input sequences as a sequence of embeddings, which are then

transformed using self-attention mechanisms and feed-forward networks.          
 

2. **Abstraction**: Both frameworks rely on abstraction to capture essential properties and relationships.

        * Category Theory abstracts away from concrete structures and focuses on the underlying patterns and relationships between them.

        * The Transformer model abstracts away from raw input sequences, focusing on the semantic relationships between tokens and their contextualized repre

representations.

**Inspirations and analogies:**

1. **Functoriality**: The Transformer's self-attention mechanism can be seen as a functor that maps input sequences to output sequences, much like    

functors in Category Theory map objects from one category to another.

2. **Composition of morphisms**: The Transformer's encoder-decoder architecture can be viewed as composing multiple "morphisms" (transformations) to

generate the final output sequence, mirroring the composition of morphisms in Category Theory.

3. **Higher-order thinking**: Both frameworks demonstrate higher-order thinking by representing complex structures as compositions of simpler ones,

which enables them to reason about and manipulate these structures.

**Influence:**

While there is no direct evidence that the Transformer model was inspired by Category Theory, it's possible that the authors were influenced by the

ideas and concepts in Category Theory. The connections between these two frameworks are intriguing, and exploring their relationships could lead to new

insights and innovations in natural language processing and beyond.

Keep in mind that this analogy is not a direct mapping; rather, it highlights some interesting parallels and potential inspirations between Category

Theory and the Transformer model.

>>> Send a message (/? for help)

题外话：有学者研究称Llama work in English: https://arxiv.org/abs/2402.10588    

笔者觉得不是英语，是大模型自己的高维概率向量张成的语言空间。但，即便如此， embedding 成为高维概率向量，学习到的语法机构，语义群分类，总会受到英文的影响，所以内部呈现出类似英语的“生成”方式一点都不奇怪。

还好，能够事后翻译成中文：

>>> 能把你这个回答翻译成中文吗

?

范畴学和Transformer 模型之间确实存在一些关联。

**共同点：**

1. **组合性**：both Category Theory 和Transformer 模式强调组合性，即将复杂结构分解成更小、更可管理的组件，然后将其组合起来形成更多复杂的实体。

        * 在范畴学中，这通过morphisms（函数）和functors（转换之间的类别）实现。

        * 在Transformer 模式中，这通过将输入序列表示为嵌入向量，然后使用自注意机制和 feed-forward 网络来转换实现。

2. **抽象性**：both 框架依赖于抽象性，以捕捉基本特征和关系。

        * 范畴学抽象化 away from具体结构，focus on underlying patterns 和 relationships 之间。

        * Transformer 模式抽象化 away from raw 输入序列，focus on token 之间的语义关系和它们的上下文表示。

**启发和类比：**

1. **函子性**：Transformer 的自注意机制可以被视为一个函子，它将输入序列映射到输出序列，类似于范畴学中的函子，它将对象从一个类别映射到另一个类别。

2. **morphisms 组合**：Transformer 的编码-解码架构可以被视为组合多个“morphisms”（变换）来生成最终输出序列，类似于范畴学中的 morphisms 组合。    

3. **高阶思维**：both 框架展示了高阶思维，即将复杂结构表示为更小的组件，然后使用这些组件来reason about 和manipulate 这些结构。

**影响**：

虽然没有直接证据表明Transformer 模式是由范畴学启发的，但可能性仍然存在。两者之间的关联很有趣，探索它们的关系可能会带来新的见解和innovation 在自然语言处理领域以外。

请注意，这个类比不是一个直接映射，而是 highlight some interesting parallels 和 potential inspirations 之间的关系。