Multi-head Latent Attention
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- How Large Language Model Context Windows Work
An explainer collected in the WeChat public account AngryCode describes the context window as the amount of text a language model can process at one time, measured in tokens rather than characters. It explains that system instructions, conversation history, the current prompt, pasted documents, and generated output share the same capacity, so older information may be dropped or summarized when the limit is reached. The article also offers practical guidance on retaining key facts, summarizing older conversations, including only relevant document sections, and leaving room for the response.
掘金 · 人工智能 · 🔥 0 - CPU Reproduction Explores Kimi K3’s Kimi Delta Attention
A Juejin AI article presents the first part of a lightweight, CPU-only reproduction of Kimi K3’s Kimi Delta Attention, without GPU use or large-scale training. It analyzes state recurrence, delta-rule writes, bounded logarithmic decay, channel-wise forgetting, head-wise RMSNorm, output gating, and comparisons with other attention and state-space mechanisms, alongside runnable implementation code and demos.
掘金 · 人工智能 · 🔥 0
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