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@atajanov_22.03: Barcelona ❤️💙 #rek #foryou #barcelona
Atajanov
Open In TikTok:
Region: TM
Tuesday 20 October 2026 09:52:57 GMT
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Comments
batyrov🧢 :
psg 4 :2😂
2026-10-20 23:21:26
5
Meyso🤙🏻🖤 :
su gun garas gorers ollany
2026-10-20 16:01:16
3
Abdy_7 :
Real utmagy arzuw edersin
2026-10-21 02:21:49
3
ᬊᬁ꯭Ꮐ꯭ᥙ꯭ꭱ꯭ᏼꪒ̵꯭ꪀ꯭ꪀ꯭ᥱ꯭ꮲᥱ꯭ຣ꯭🪖 :
Visca barca ❤️💙
2026-10-21 03:48:16
3
𝐟𝐞𝐧𝐨𝐱 :
2-1 agyr degyami?
2026-10-26 00:50:12
0
𝗘𝘀𝗲𝗻𝗼𝘃𝘃𒌐 :
4:2
2026-10-20 21:06:29
1
user3408797979523 :
😂😂😂😂😂😂
2026-10-20 10:00:32
2
᪤.Ꭼ ꓒ ᥱ ꮲ ຣ ꪱꪲ ຂ ຂ ⚜️ ᜪ̵ ⧽ :
2 1 😅
2026-10-25 22:40:09
1
nunisx🌷 :
Barca💙❤️
2026-10-24 05:46:00
1
SOYUNOW__7 :
2026-10-22 05:53:36
2
Abdy_7 :
Psg 4-2 Barcelona [Слезы радости]
2026-10-21 02:22:05
3
ᴋᴀᴋᴀᴅᴜʀᴅʏ.ᴇᴅɪᴛs :
4:2 psg😂
2026-10-21 07:33:33
1
Kako_🪐 :
ufffffff
2026-10-22 04:55:22
1
Sxrin.privvv🇪🇦 :
Visca_Barca🇪🇸
2026-10-23 02:09:46
1
jr :
2026-10-21 17:21:29
1
Azym sheykh🇪🇪🗿 :
4 :2
2026-10-21 08:01:48
0
Kiçi_Esto👑 :
visca barca
2026-10-20 14:34:55
3
dilşatbek_ :
Visca Barca 💙❤️
2026-10-25 11:19:18
1
Eldarcik_tm_011 :
@Abdy_7:Psg 4-2 Barcelona [Слезы радости]
2026-10-22 13:23:38
1
Dowran_tt :
2026-10-22 08:21:10
1
ᴰᵃʳᵏ ᴬᵐᶻ 🎧❗ :
Viska barsa 💙❤️
2026-10-23 04:18:33
1
𝙔𝙜𝙩𝙮ý𝙖𝙧.𝘼𝙚 :
2026-10-21 11:06:23
1
Bilmek_gerek⚜️däl.! :
wisca barsa
2026-10-23 14:00:00
1
Arsi_7Ꞵ :
Viska barsa viska katalunya🔴🔵
2026-10-22 10:21:41
1
Relax❗ :
wiska barsa wiska Katalonya
2026-10-20 12:47:05
1
To see more videos from user @atajanov_22.03, please go to the Tikwm homepage.
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Nobody ever wrote the code that lets ChatGPT write Python. Not one line of it. 🤯 Here's how a large language model actually gets built, step by step: 1️⃣ Data. Trillions of words of books, web pages, code and forums, then cleaned: duplicates removed, spam filtered, broken text stripped. One open dataset, FineWeb, is 15 trillion tokens of web text. 2️⃣ Tokens. The model never sees words. "Unbelievable" becomes 4 pieces: Un · bel · iev · able. Each piece becomes a number, and each number becomes a vector of hundreds of numbers. 3️⃣ The transformer. Stacks of layers, and inside each one, attention. In "the programmer fixed the server because it crashed", a real GPT-2 attention head sends 54% of "it"'s attention straight to "server". 4️⃣ Pre-training. One task, repeated trillions of times: predict the next token. Guess wrong, measure how wrong, nudge the weights a tiny bit downhill. Meta trained Llama 3 on up to 16,000 GPUs. 5️⃣ Post-training. A pre-trained model is just a very powerful autocomplete. Fine-tuning on good examples, human feedback and checkable rewards (did the math match? did the code pass?) turns it into an assistant. 6️⃣ Serving. A 405-billion-parameter model needs about 810 GB just for its weights. One GPU holds 80. So it gets split, compressed and batched so millions of people can use it at once. Every example in the video comes from a real model: the tokens, the attention, even the loss landscape. The weird part? Writing code, explaining physics, translating French: none of it was programmed. It all emerges from one tiny objective, repeated at enormous scale: predict what comes next, and get slightly less wrong every time. Which step surprised you most? Drop the number 👇 Save this for the next time someone calls AI "just autocomplete." #llm #artificialintelligence #machinelearning #chatgpt #techexplained
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