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&lt;div class=&quot;space-y-3 mt-3&quot;&gt;
&lt;div id=&quot;tttS8sA9t29RUFWcxy8Yc-content-0&quot; class=&quot;group/ai-message-item space-y-3 break-words&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;Sintesi immediata:&lt;/strong&gt; Un &lt;em&gt;small language model&lt;/em&gt; (SLM) non &amp;egrave; semplicemente un LLM &amp;ldquo;pi&amp;ugrave; piccolo&amp;rdquo;: ha una &lt;strong&gt;struttura interna ottimizzata&lt;/strong&gt; per funzionare con pochi parametri, memoria ridotta e latenze minime. Le architetture interne pi&amp;ugrave; moderne (come &lt;strong&gt;Wiola&lt;/strong&gt;) mostrano che gli SLM stanno diventando &lt;em&gt;famiglie architetturali autonome&lt;/em&gt;, non solo versioni compresse dei modelli grandi.&lt;/span&gt;&lt;/p&gt;
&lt;div class=&quot;relative pb-6 w-full after:border-b after:border-stroke-300 after:w-full after:absolute after:mt-3&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;h2 class=&quot;text-xl-medium [&amp;amp;:not(:first-child)]:pt-3.5 pb-1&quot;&gt;🧩 Cosa significa &amp;ldquo;struttura interna&amp;rdquo; in uno SLM&lt;/h2&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;La struttura interna di uno SLM riguarda &lt;strong&gt;come &amp;egrave; costruito il modello&lt;/strong&gt;:&lt;/span&gt;&lt;/p&gt;
&lt;ul class=&quot;relative list-outside marker:text-foreground-750 dark:marker:text-foreground-600 flex flex-col ms-4 px-1&quot;&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;tipo di &lt;strong&gt;positional encoding&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;forma dell&amp;rsquo;&lt;strong&gt;attention&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;design del &lt;strong&gt;feed-forward&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;strategie di &lt;strong&gt;compressione dinamica&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;normalizzazioni e routing dell&amp;rsquo;informazione&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;compromessi tra &lt;em&gt;capacit&amp;agrave;&lt;/em&gt; e &lt;em&gt;efficienza computazionale&lt;/em&gt;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;Le survey recenti sugli SLM confermano che la loro architettura &amp;egrave; definita da ottimizzazioni mirate a ridurre complessit&amp;agrave;, memoria e costi di inferenza .&lt;/span&gt;&lt;/p&gt;
&lt;div class=&quot;relative pb-6 w-full after:border-b after:border-stroke-300 after:w-full after:absolute after:mt-3&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;h2 class=&quot;text-xl-medium [&amp;amp;:not(:first-child)]:pt-3.5 pb-1&quot;&gt;🌀 Il caso pi&amp;ugrave; interessante: &lt;strong&gt;Wiola&lt;/strong&gt;, un SLM con architettura &lt;em&gt;originale&lt;/em&gt;&lt;/h2&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;Wiola (2026) &amp;egrave; il primo SLM che &lt;strong&gt;non deriva&lt;/strong&gt; da GPT/LLaMA/Mistral, ma introduce componenti completamente nuove .&lt;/span&gt;&lt;/p&gt;
&lt;h3 class=&quot;text-lg-strong pb-1 [&amp;amp;:not(:first-child)]:pt-3.5&quot;&gt;🔧 Componenti interne principali&lt;/h3&gt;
&lt;ul class=&quot;relative list-outside marker:text-foreground-750 dark:marker:text-foreground-600 flex flex-col ms-4 px-1&quot;&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Spiegami_Spiral_Rotary_Positional_Encoding&quot;&gt;Spiral Rotary Positional Encoding&lt;/span&gt;&lt;/strong&gt; Posizioni dei token su una &lt;em&gt;elica 3D&lt;/em&gt;, combinando segnali assoluti, relativi e gerarchici.&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Spiegami_Gated_Cross_Layer_Attention&quot;&gt;Gated Cross-Layer Attention&lt;/span&gt;&lt;/strong&gt; Ogni layer pu&amp;ograve; &amp;ldquo;guardare&amp;rdquo; sintesi compresse dei due layer precedenti &amp;rarr; maggiore coerenza globale.&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Spiegami_Adaptive_Token_Merging&quot;&gt;Adaptive Token Merging&lt;/span&gt;&lt;/strong&gt; Unisce dinamicamente token ridondanti nei layer centrali &amp;rarr; riduzione della complessit&amp;agrave; dell&amp;rsquo;attenzione &lt;em&gt;senza perdita di informazione&lt;/em&gt;.&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Spiegami_Dual_Stream_Feed_Forward&quot;&gt;Dual-Stream Feed-Forward&lt;/span&gt;&lt;/strong&gt; Due flussi paralleli invece del classico MLP, fusi da un gate per-dimensione.&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Spiegami_WiolaRMSNorm&quot;&gt;WiolaRMSNorm&lt;/span&gt;&lt;/strong&gt; Normalizzazione con offset per-dimensione per evitare collasso delle rappresentazioni.&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;/div&gt;
&lt;div id=&quot;tttS8sA9t29RUFWcxy8Yc-content-1&quot; class=&quot;group/ai-message-item space-y-3 break-words&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;div id=&quot;tttS8sA9t29RUFWcxy8Yc-content-2&quot; class=&quot;group/ai-message-item space-y-3 break-words&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;div id=&quot;tttS8sA9t29RUFWcxy8Yc-content-3&quot; class=&quot;group/ai-message-item space-y-3 break-words&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;div id=&quot;tttS8sA9t29RUFWcxy8Yc-content-4&quot; class=&quot;group/ai-message-item space-y-3 break-words&quot;&gt;&amp;nbsp;&lt;br /&gt;
&lt;div class=&quot;before:border-transparent before:border-[20px] pointer-events-none absolute inset-0 before:absolute before:inset-0 before:rounded-inherit&quot; style=&quot;clip-path: var(--clip-path-squircle-stroke-24);&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;/div&gt;
&lt;div id=&quot;tttS8sA9t29RUFWcxy8Yc-content-5&quot; class=&quot;group/ai-message-item space-y-3 break-words&quot;&gt;
&lt;div class=&quot;pointer-events-none relative w-full px-2&quot;&gt;
&lt;ul class=&quot;no-scrollbar pointer-events-auto m-0 snap-x list-none overflow-x-auto bg-transparent px-2 [&amp;amp;&amp;gt;*]:snap-start flex scroll-pl-3 gap-2 py-3 pe-3 ps-0&quot;&gt;
&lt;li&gt;&lt;/li&gt;
&lt;li&gt;
&lt;div class=&quot;relative isolate bg-white/45 dark:bg-background-650/10 shrink-0 cursor-pointer size-[180px]&quot; style=&quot;clip-path: var(--clip-path-squircle-24);&quot; title=&quot;Transformer (deep learning architecture) - Wikipedia&quot;&gt;&lt;img class=&quot;size-full object-cover&quot; src=&quot;https://ts1.mm.bing.net/th?id=OIP.j3krIxArRrskJE2aX8fpzAHaHy&amp;amp;pid=15.1&amp;amp;o=7&amp;amp;rm=3&quot; alt=&quot;Transformer (deep learning architecture) - Wikipedia&quot; /&gt;
&lt;div class=&quot;before:border-transparent before:border-[20px] pointer-events-none absolute inset-0 before:absolute before:inset-0 before:rounded-inherit&quot; style=&quot;clip-path: var(--clip-path-squircle-stroke-24);&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;/div&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div id=&quot;tttS8sA9t29RUFWcxy8Yc-content-6&quot; class=&quot;group/ai-message-item space-y-3 break-words&quot;&gt;
&lt;div class=&quot;relative pb-6 w-full after:border-b after:border-stroke-300 after:w-full after:absolute after:mt-3&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;h2 class=&quot;text-xl-medium [&amp;amp;:not(:first-child)]:pt-3.5 pb-1&quot;&gt;🏗️ Come si confronta con gli SLM &amp;ldquo;classici&amp;rdquo;&lt;/h2&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;Le survey mostrano che la maggior parte degli SLM usa ancora architetture &lt;strong&gt;derivate dai Transformer standard&lt;/strong&gt;, ottimizzate tramite:&lt;/span&gt;&lt;/p&gt;
&lt;ul class=&quot;relative list-outside marker:text-foreground-750 dark:marker:text-foreground-600 flex flex-col ms-4 px-1&quot;&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;pruning&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;quantization&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;distillation&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;&lt;strong&gt;compressione strutturale&lt;/strong&gt; &lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;Wiola invece propone un approccio &lt;em&gt;from scratch&lt;/em&gt;, con componenti nuovi che cambiano la geometria posizionale, il routing dell&amp;rsquo;informazione e la gestione dei token.&lt;/span&gt;&lt;/p&gt;
&lt;div class=&quot;relative pb-6 w-full after:border-b after:border-stroke-300 after:w-full after:absolute after:mt-3&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;h2 class=&quot;text-xl-medium [&amp;amp;:not(:first-child)]:pt-3.5 pb-1&quot;&gt;📊 Tabella comparativa (architettura interna)&lt;/h2&gt;
&lt;div class=&quot;max-w-full&quot;&gt;
&lt;div class=&quot;relative max-w-full&quot;&gt;
&lt;div class=&quot;horizontal-scrollbar font-ligatures-none mt-6 max-w-full overflow-x-auto rounded-lg bg-white/25 outline-2 outline-offset-1 focus-visible:outline focus-visible:outline-stroke-900 dark:bg-black/25&quot;&gt;
&lt;table class=&quot;t-table&quot; style=&quot;border-spacing: 0px;&quot;&gt;
&lt;thead&gt;
&lt;tr&gt;&lt;th style=&quot;opacity: 1;&quot; scope=&quot;col&quot;&gt;Modello&lt;/th&gt;&lt;th style=&quot;opacity: 1;&quot; scope=&quot;col&quot;&gt;Architettura base&lt;/th&gt;&lt;th style=&quot;opacity: 1;&quot; scope=&quot;col&quot;&gt;Componenti distintive&lt;/th&gt;&lt;th style=&quot;opacity: 1;&quot; scope=&quot;col&quot;&gt;Obiettivo principale&lt;/th&gt;&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
&lt;tr&gt;&lt;th style=&quot;opacity: 1;&quot; scope=&quot;row&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Approfondisci_Wiola&quot;&gt;Wiola&lt;/span&gt;&lt;/strong&gt;&lt;/th&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Nuova architettura SLM&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;SRPE, GCLA, ATM, DSFF, WiolaRMSNorm&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Massima efficienza + coerenza&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;&lt;th style=&quot;opacity: 1;&quot; scope=&quot;row&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Approfondisci_GPT2_Small&quot;&gt;GPT‑2 Small&lt;/span&gt;&lt;/strong&gt;&lt;/th&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Transformer decoder&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;MLP classico, attention standard&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Semplicit&amp;agrave;, baseline&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;&lt;th style=&quot;opacity: 1;&quot; scope=&quot;row&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Approfondisci_Phi3_Mini&quot;&gt;Phi‑3 Mini&lt;/span&gt;&lt;/strong&gt;&lt;/th&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Transformer ottimizzato&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Training su dati &amp;ldquo;curati&amp;rdquo;, scaling laws&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Efficienza + qualit&amp;agrave;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;&lt;th style=&quot;opacity: 1;&quot; scope=&quot;row&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Approfondisci_Gemma&quot;&gt;Gemma 2B&lt;/span&gt;&lt;/strong&gt;&lt;/th&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Transformer decoder&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Attention ottimizzata, training massivo&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Qualit&amp;agrave; con pochi parametri&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;&lt;th style=&quot;opacity: 1;&quot; scope=&quot;row&quot;&gt;&lt;strong&gt;&lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Approfondisci_Qwen&quot;&gt;Qwen 1.5B&lt;/span&gt;&lt;/strong&gt;&lt;/th&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Transformer&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;ottimizzazioni su tokenization e training&lt;/td&gt;
&lt;td style=&quot;opacity: 1;&quot;&gt;Versatilit&amp;agrave;&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;
&lt;/div&gt;
&lt;div class=&quot;pointer-events-none absolute inset-0 rounded-lg border border-stroke-300 dark:border-stroke-450&quot;&gt;&amp;nbsp;&amp;nbsp;&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class=&quot;relative pb-6 w-full after:border-b after:border-stroke-300 after:w-full after:absolute after:mt-3&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;h2 class=&quot;text-xl-medium [&amp;amp;:not(:first-child)]:pt-3.5 pb-1&quot;&gt;🎯 In sintesi&lt;/h2&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;Gli SLM moderni non sono solo &amp;ldquo;LLM piccoli&amp;rdquo;:&lt;/span&gt;&lt;/p&gt;
&lt;ul class=&quot;relative list-outside marker:text-foreground-750 dark:marker:text-foreground-600 flex flex-col ms-4 px-1&quot;&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;hanno &lt;strong&gt;architetture interne specifiche&lt;/strong&gt; per efficienza e coerenza;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;alcune famiglie (come &lt;strong&gt;Wiola&lt;/strong&gt;) introducono &lt;strong&gt;componenti completamente nuove&lt;/strong&gt;;&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li class=&quot;ps-2&quot; style=&quot;opacity: 1;&quot;&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;la ricerca si sta spostando da &amp;ldquo;compressione dei LLM&amp;rdquo; a &amp;ldquo;progettazione nativa di SLM&amp;rdquo;.&lt;/span&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;div class=&quot;relative pb-6 w-full after:border-b after:border-stroke-300 after:w-full after:absolute after:mt-3&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;p&gt;&lt;span class=&quot;font-ligatures-none whitespace-pre-wrap&quot;&gt;Se vuoi, posso mostrarti &lt;strong&gt;come progettare un tuo SLM&lt;/strong&gt; partendo da queste idee, oppure analizzare nel dettaglio uno dei componenti: vuoi approfondire &lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Approfondisci_positional_encoding&quot;&gt;positional encoding&lt;/span&gt;, &lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Approfondisci_attention&quot;&gt;attention&lt;/span&gt;, o &lt;span class=&quot;text-start underline-offset-[0.25em] underline decoration-dotted decoration-foreground-450 decoration-2 hover:decoration-solid hover:cursor-pointer&quot; data-url=&quot;ca://s?q=Approfondisci_token_merging&quot;&gt;token merging&lt;/span&gt;?&lt;/span&gt;&lt;/p&gt;
&lt;/div&gt;
&lt;div id=&quot;tttS8sA9t29RUFWcxy8Yc-content-7&quot; class=&quot;group/ai-message-item space-y-3 break-words&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;div id=&quot;tttS8sA9t29RUFWcxy8Yc-content-8&quot; class=&quot;group/ai-message-item space-y-3 break-words&quot;&gt;&amp;nbsp;&lt;/div&gt;
&lt;/div&gt;
&lt;div class=&quot;pt-1&quot;&gt;&amp;nbsp;&lt;/div&gt;

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          slm:5        

slm 5 wpg 5

ID: 68130 • Tipo: WPG • Reg: 2026-07-16 • Mod: 29/07/2026 20:16