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@hamzakok85: #foryoupageviralシ゚videotiktok #💃🏾🐝 #🐝🍫💞
꧁࿇♥𝑯𝒂𝒎𝒛𝒂 𝑲𝒐𝒌𝒐..💞🎉
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Region: PK
Monday 17 August 2026 14:22:25 GMT
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✨⚡•ꭺḟ𐌽𝓪𐌽•🌸🥀 :
AwOo 🫀
2026-08-21 11:09:31
0
dollar queen S.❤ :
Beautiful girl lips ❤️😘
2026-08-19 16:52:08
0
asimjan644 :
nice💋💋💋
2026-08-18 08:11:19
0
sahil jan :
2026-08-19 06:51:07
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tairq_____khan804 :
Hi Jna 💋
2026-08-18 19:21:12
0
ªĥmªɗẓ̌ªı :
Follow me ❤️…..
2026-08-18 16:49:10
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لیاقت جئ ⚔️ :
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2026-08-21 12:42:02
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farhyankhan :
💓💓💓
2026-08-21 11:58:20
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2026-08-21 11:04:00
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2026-08-21 05:58:44
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2026-08-20 16:11:05
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2026-08-19 16:51:49
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Z. 🕊️🕊️. 🥀🥀. S :
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2026-08-20 12:39:33
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2026-08-20 08:30:03
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2026-08-20 07:05:07
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khaista Gul :
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2026-08-20 05:34:40
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2026-08-20 05:31:45
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2026-08-20 03:24:20
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2026-08-19 17:36:42
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2026-08-20 16:29:36
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2026-08-19 15:39:36
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🏴☠️Ramzan—Tanha🏴☠️ :
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2026-08-17 14:24:57
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To see more videos from user @hamzakok85, please go to the Tikwm homepage.
Other Videos
Mi mejor amigo casi me gana en está 💜
An LLM has never seen a word in its life. ⚡ It sees integers. Here is what actually happens to "I love AI" 👇 🟢 1. TOKENIZATION The text is cut into tokens, and each token becomes an integer. I → 40 · love → 3021 · AI → 15836 That row of numbers is literally all the model receives. 💥 Tokens are not words. "strawberry" is three tokens. That's why models miscount letters in it — they never see the letters. 🔵 2. EMBEDDINGS Each ID is looked up in a table and becomes a vector of ~4096 learned numbers. The ID is just an address. The vector is the meaning. Tokens with similar meanings sit close together in that space. Nobody programmed that — it fell out of training. 🟡 3. SELF-ATTENTION Every token scores every other token, itself included. It's a matrix. Rows are "who's asking", columns are "who they're looking at", and each row sums to 1. When "AI" asks, it might put 0.52 on itself, 0.37 on "love", 0.11 on "I". Those weights decide what context flows forward. 💥 This is the whole trick. Not memory. Not a database. A weighted average, recomputed for every token, at every layer. 🟣 4. LAYERS → OUTPUT Attention → add & norm → feed forward → add & norm. Stack it 32 times, or 80. Out comes a probability distribution over the entire vocabulary: is 0.41 · will 0.22 · can 0.14 · has 0.09 · ... 👀 The one nobody says: the model does not pick a word. It produces a distribution and something else samples from it. Temperature, top-p, top-k — those aren't model settings, they're sampling settings applied AFTER the model has finished thinking. Same model, same distribution, different word out. That's why the same prompt gives different answers. 🎯 The rule: it never sees words. Only numbers. 📸 Screenshot the attention matrix. It's the part everyone skips. Follow @hackproduct — we turn scary AI-engineering concepts into things you can ship. ⚡ . . #LLM #transformers #AIengineering #machinelearning #deeplearning
Sữa rửa mặt CeraVe #cerave #suaruamatcerave #suaruamat #skincare #viral
Từ ngày dùng em dầu gội này không còn biết bị bết tóc là gì luôn #verdantpark #daugoi #daugoikiemdau #daugoiphongtoc #verdantparkshampoo @Verdantpark Official
魔术真好玩 #夫妻日常 #万万没想到 #hottrend #tipquayclip #douyin
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