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@poisonous_flowers_: #CapCut // Give Me All Your Love - Whitesnake

𓂃.ִֶָ»̶̶͓͓̽̽⅌öišöňöüšᥫ᭡.ִֶָ𓂃
𓂃.ִֶָ»̶̶͓͓̽̽⅌öišöňöüšᥫ᭡.ִֶָ𓂃
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Region: TH
Tuesday 15 April 2025 13:07:22 GMT
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Music

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No Watermark .mp4 (32.89MB) No Watermark(HD) .mp4 (26.19MB) Watermark .mp4 (38.07MB) Music .mp3

Comments

limbani291975
luca ❤️ :
whitesnake ❤️❤️❤️❤️
2025-04-15 18:29:08
4
drumsoloist
malcolmf :
whitesnake loved them since day one
2025-04-17 18:29:16
0
michaelditchfield64
michaelditchfield64 :
Absolutely class✊
2025-04-17 21:25:41
0
user96920053944435
user9692005394443 :
temazo
2025-04-16 12:35:10
2
vikitorviktorchik
Vikitor Viktorchik :
Девид всё таки крут💪🤟👍
2025-04-16 18:10:19
5
pablo.isaac.estra
Pablo Isaac Estrada Castillo :
Buena música 👌🎵 espectacular
2025-04-15 13:45:47
1
17653jm
jm :
Otro, para q hablar.
2025-04-16 16:51:26
1
filloieyfr9
fillo :
questa è una formazione strepitosa
2025-04-16 10:37:57
1
limbani291975
luca ❤️ :
lo zio david❤️
2025-04-15 18:29:27
1
marinaperkov
Marinaa Books 📖 :
🤟🤟🤟🤟
2026-03-21 10:45:34
1
mariam43404
Mariam :
🥰🥰🥰
2025-04-16 23:21:17
2
strons66s
strons :
🔥🔥🔥
2025-04-18 14:10:16
2
giannisliapis5
Γιαννης Λιαπης :
🤟🤟🤟
2025-04-17 01:04:20
1
sergei13fedorenko
sergei13fedorenko :
🔥🔥🔥
2025-04-17 01:22:17
1
yuriyryzh
Юрій :
👍👍👍
2025-04-17 05:17:54
1
sergiusalbalupus
Срђан Мркаја [Srdjan Mrkaja] :
🥰
2025-04-16 23:16:50
1
user8814123369838
user8814123369838 :
👍🙏
2025-04-17 02:13:47
1
user66836517695834
Вадим Колодій :
🥰🥰🥰
2025-04-16 21:34:49
1
vladasbaltusis
vladasbaltusis :
💯
2025-04-17 05:49:47
1
edmunds57
EDMUNDS :
👍👍👍
2025-04-17 08:32:21
1
user25999517018415
user25999517018415 :
🥰
2025-04-16 18:41:00
1
andreakratzer
Andrea Kratzer :
👍👍👍👍👍👍👍🤩🤩🤩🤩🤩🤩💥💥💥💥💥💥💥💥💥💥💥💥💥💥💥💥
2025-04-16 18:08:55
1
frank.asselin
Frank Asselin :
🙏😎🙏
2025-04-16 11:39:04
1
lalo.marinov
Lalo Marinov :
🥰🥰🥰
2025-04-16 16:03:36
1
To see more videos from user @poisonous_flowers_, please go to the Tikwm homepage.

Other Videos

في حد يحب كده من يوم 💕🤭 . . . . . . #تصميم_فيديوهات🎶🎤🎬 #اغاني #حب #parati #صلي_علي_النبي_محمد_صلي_الله_عليه_وسلم
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This AI model was given a research paper and was tasked to reproduce the entire thing itself. 12 hours later, it created 18 commits, 23 figures, and replicated every core experiment. The model isn't GPT, Claude or Gemini. It's Minimax M3, an open-weight frontier model featuring a 1 million token context window and native multimodality. It processes queries efficiently using Minimax sparse attention (MSA), which is an extension of grouped-query attention (GQA) where multiple query heads share one key-value head. MSA adds 2 small projection matrices that form an index branch to choose which past tokens each query should compute scores against. Keys and values are split into blocks of 128 tokens. The index branch scores every block by its highest-scoring token and keeps the top 16 blocks. Attention is then run over those chosen blocks. Compared to dense attention, MSA computes only on a subset of the full tokens. At 1 million tokens, that's 14x faster prefill and 28x less attention FLOPs with comparable quality to full attention. Minimax M3 can be used in coding harnesses like Claude code, Codex, OpenCode, and more. You only need an API key, which can also use token plans. M3 can reason across long, multimodal contexts at once while generating and modifying the code. It excels in multimodal applications and follows UI systems really well at a low token cost. If M3’s native multimodality and long context sounds interesting, check the link in my bio to try MiniMax M3. #MiniMax #MiniMaxM3 #MiniMaxCode
This AI model was given a research paper and was tasked to reproduce the entire thing itself. 12 hours later, it created 18 commits, 23 figures, and replicated every core experiment. The model isn't GPT, Claude or Gemini. It's Minimax M3, an open-weight frontier model featuring a 1 million token context window and native multimodality. It processes queries efficiently using Minimax sparse attention (MSA), which is an extension of grouped-query attention (GQA) where multiple query heads share one key-value head. MSA adds 2 small projection matrices that form an index branch to choose which past tokens each query should compute scores against. Keys and values are split into blocks of 128 tokens. The index branch scores every block by its highest-scoring token and keeps the top 16 blocks. Attention is then run over those chosen blocks. Compared to dense attention, MSA computes only on a subset of the full tokens. At 1 million tokens, that's 14x faster prefill and 28x less attention FLOPs with comparable quality to full attention. Minimax M3 can be used in coding harnesses like Claude code, Codex, OpenCode, and more. You only need an API key, which can also use token plans. M3 can reason across long, multimodal contexts at once while generating and modifying the code. It excels in multimodal applications and follows UI systems really well at a low token cost. If M3’s native multimodality and long context sounds interesting, check the link in my bio to try MiniMax M3. #MiniMax #MiniMaxM3 #MiniMaxCode
#ئەکتیڤبن🥀🖤ـہہـ٨ــہ
#ئەکتیڤبن🥀🖤ـہہـ٨ــہ
Roddy Ricch
Roddy Ricch "Down Below" #roddyricch #usa #us #trap #lyrics #hiphop #rap #givingthelyrics #fyp

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