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        <title>NoPE - 标签 - vllbc02&#39;s blogs</title>
        <link>https://blog.vllbc.top/tags/nope/</link>
        <description>NoPE - 标签 - vllbc02&#39;s blogs</description>
        <generator>Hugo -- gohugo.io</generator><language>zh-CN</language><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Sat, 06 Jun 2026 00:00:00 &#43;0000</lastBuildDate><atom:link href="https://blog.vllbc.top/tags/nope/" rel="self" type="application/rss+xml" /><item>
    <title>Arcee AI Trinity Large</title>
    <link>https://blog.vllbc.top/arcee-trinity-large/</link>
    <pubDate>Sat, 06 Jun 2026 00:00:00 &#43;0000</pubDate>
    <author>vllbc</author>
    <guid>https://blog.vllbc.top/arcee-trinity-large/</guid>
    <description><![CDATA[<h1 id="arcee-ai-trinity-large">Arcee AI Trinity Large</h1>
<blockquote>
<p>原文第 20 节。Arcee AI 在 2026-01-27 发布的 400B MoE（13B
激活）。技术报告相当详细。</p>
</blockquote>
<h2 id="关键设计">关键设计</h2>
<h3 id="swa介于-gemma-3-与-olmo-3-之间">3:1 SWA（介于 Gemma 3 与 Olmo 3
之间）</h3>
<ul>
<li>local:global = 3:1（不像 Gemma 3 / Xiaomi 是 5:1，也不像纯
global）</li>
<li>滑动窗口大小 = 4096（与 Olmo 3 类似）</li>
</ul>
<h3 id="nopeno-positional-embeddingnope-in-global-layers">[[NoPE（No
Positional Embedding）|NoPE]] in global layers</h3>
<ul>
<li>global attention 层用 NoPE（参考 [[SmolLM3]] 的做法）</li>
<li>局部层照常用 RoPE</li>
</ul>
<h3 id="gated-attention">Gated Attention</h3>
<ul>
<li>不像 Qwen3-Next 是完整 GatedDeltaNet，而是借鉴了 Qwen3-Next 的
attention 门控</li>
<li>在 scaled dot-product <strong>后、output linear projection
前</strong> 加 elementwise gating</li>
<li>效果：减少 attention sinks，改善长序列泛化 + 训练稳定性</li>
</ul>
<p></p>]]></description>
</item>
<item>
    <title>NoPE（No Positional Embedding）</title>
    <link>https://blog.vllbc.top/nopeno-positional-embedding/</link>
    <pubDate>Sat, 06 Jun 2026 00:00:00 &#43;0000</pubDate>
    <author>vllbc</author>
    <guid>https://blog.vllbc.top/nopeno-positional-embedding/</guid>
    <description><![CDATA[<h1 id="nopeno-positional-embedding">NoPE（No Positional
Embedding）</h1>
<blockquote>
<p>来自 Sebastian Raschka 的 <em>The Big LLM Architecture
Comparison</em>。</p>
</blockquote>
<h2 id="一句话">一句话</h2>
<p>NoPE = 完全不加位置编码。没有绝对位置 embedding，也没有 RoPE。起源是
2023 年的 <em>The Impact of Positional Encoding on Length Generalization
in Transformers</em> 论文。</p>]]></description>
</item>
<item>
    <title>SmolLM3</title>
    <link>https://blog.vllbc.top/smollm3/</link>
    <pubDate>Sat, 06 Jun 2026 00:00:00 &#43;0000</pubDate>
    <author>vllbc</author>
    <guid>https://blog.vllbc.top/smollm3/</guid>
    <description><![CDATA[<h1 id="smollm3">SmolLM3</h1>
<blockquote>
<p>原文第 7 节。HuggingFace 的 3B 模型，介于 Qwen3 1.7B 和 4B 之间。</p>
</blockquote>
<h2 id="总体定位">总体定位</h2>
<ul>
<li>3B 参数 size：兼顾能力与本地可运行性</li>
<li>训练透明度：与 OLMo 类似，公开了大量训练细节</li>
<li>性能：在与 Qwen3 1.7B / 4B、Llama 3 3B、Gemma 3 4B
的对比中处于前列</li>
</ul>
<p></p>]]></description>
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