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@zero4officiel0:

ZERO4OFFICIEL
ZERO4OFFICIEL
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Region: MA
Sunday 13 September 2026 20:37:58 GMT
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Comments

lasicile7
Lasicile :
javou elle fait mal
2026-09-18 22:35:23
10
ketama92ii
ketama92ii :
c'est quoi cette couleur de malade donne la ref wsh
2026-09-20 14:47:50
0
emiliedeville21
Emilie ( Mi Ange ) :
trop belle
2026-09-17 08:20:16
17
flexiiieeee
flex :
MTM ?
2026-09-16 21:17:30
5
missoumaima22
💜 :
2026-09-16 19:45:56
5
nourrrskh
Nourrskh :
La couleur trop atypique j’aime trop😍
2026-09-16 16:02:26
8
aurel.gyt
𝒜𝓊𝓇𝑒𝓁 𝐹𝓁𝑜𝓌 🌹🎧 :
Pépite 😍🔥🔥🔥🔥
2026-09-20 18:03:50
2
kristi57896
kristi :
une beauté 😍
2026-09-18 23:11:08
2
jouns78
jouns78 :
2026-09-18 02:27:12
3
wcdr.18k
W,cdr.18kvz :
2026-09-16 10:43:23
4
jujuvip13
Jujuvip13 :
2026-09-16 18:25:48
2
le_vrai4
M93🇩🇿🇲🇦🇵🇸❤️⚽️🤪 :
2026-09-17 11:15:38
2
kevinlpr1
fier d'être français 🇨🇵🇨🇵 :
2026-09-17 13:06:50
2
stefano.nurra
stefano nurra :
2026-09-19 21:02:17
1
dylanabdelali73
Dylan :
2026-09-19 20:43:43
2
anime89003
anime :
😎😎😎
2026-09-19 11:13:59
1
alexa.mxmva
alexa.mxmva :
2026-09-19 17:27:03
1
maximeleflon
Maxime Leflon :
Magnifique
2026-09-19 07:17:39
3
louloute6304
🐦‍🔥louloute la chieuse 🦂 :
trop belle, un bijou !!!
2026-09-18 13:05:17
2
naoufel68200
🇩🇿naoufel68 :
2026-09-16 14:04:34
3
al4n_ttm
al4n_ttm :
Possible de la louer ouuuuu
2026-09-19 22:49:16
1
alexaudi8912
A.K.89 :
2026-09-16 14:22:57
2
carsss912
AutofasttPv :
Waw
2026-09-21 14:05:57
0
To see more videos from user @zero4officiel0, please go to the Tikwm homepage.

Other Videos
@NONOCOCO🧊🥥
@NONOCOCO🧊🥥
Ghé TGSF làm vài tấm liền nha mấy bồ #thegioiskinfood #tgsf #myphamchinhhang #xuhuong #fypシ
Ghé TGSF làm vài tấm liền nha mấy bồ #thegioiskinfood #tgsf #myphamchinhhang #xuhuong #fypシ
কি অ্যাডাম ক্যারেক্টার আছে তোমার কাছে 🥹#freefire_lover #vairalvideo #tending #tahsin_ff1 #foryoupage @TikTok @TikTok Bangladesh @Garena Free Fire Bangladesh
কি অ্যাডাম ক্যারেক্টার আছে তোমার কাছে 🥹#freefire_lover #vairalvideo #tending #tahsin_ff1 #foryoupage @TikTok @TikTok Bangladesh @Garena Free Fire Bangladesh
The world gets tired of your tears but Allah never .You can cry to Allah a thousand times and He will still listen like it’s your first🌸🤌🏻🤍 . . . . . . . .#fyp #viewsproblem #dontunderreviewmyvideo #viral #sabar
The world gets tired of your tears but Allah never .You can cry to Allah a thousand times and He will still listen like it’s your first🌸🤌🏻🤍 . . . . . . . .#fyp #viewsproblem #dontunderreviewmyvideo #viral #sabar
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.
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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