这篇文章从头实现 LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures。需要说明的是，这里写的是一个简洁的最小化训练脚本，目标是了解 JEPA 的本质：对同一文本创建两个视图，预测被遮蔽片段的嵌入，用表示对齐损失来训练。 本文的目标是让你真正 ...

随着上下文窗口的不断扩大，大型语言模型（LLM）面临着显著的性能瓶颈。尽管键值（KV）缓存对于避免重复计算至关重要，但长上下文缓存的存储开销会迅速超出GPU内存容量，迫使生产系统在多级内存结构中采用分层缓存策略。然而，将大量缓存的上下文重新 ...

Tokens are the fundamental units that LLMs process. Instead of working with raw text (characters or whole words), LLMs convert input text into a sequence of numeric IDs called tokens using a ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

What makes a large language model like Claude, Gemini or ChatGPT capable of producing text that feels so human? It’s a question that fascinates many but remains shrouded in technical complexity. Below ...