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@karina_zhamko13:

karina_zhamko13
karina_zhamko13
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Region: DE
Sunday 16 August 2026 11:40:02 GMT
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No Watermark .mp4 (1.78MB) No Watermark(HD) .mp4 (1.68MB) Watermark .mp4 (1.87MB) Music .mp3

Comments

lovegoghi
lovegoghi :
Where is the jacket from🥰
2026-08-17 11:40:04
44
chicbabyy7
ℋ :
jeanss fromm?
2026-08-17 09:27:50
2
chakroun.baya
Ch baya :
Jacket from where?
2026-08-16 16:50:10
2
havalava50
𝑯𝒂𝒗𝒂🤍🍵 :
From where is the jeans ????❤️
2026-08-18 20:07:44
0
bentereinhart
Bente Reinhart ❥ :
To perfect! 💋 Where are your jeans from? And do you have a code ?
2026-08-17 13:16:36
7
user3895594484357
Ануш :
Откуда джинсы?
2026-08-16 15:55:44
5
caughtbymari
maria 🥂🧿💕 :
вы чудо😍😍
2026-08-16 18:31:11
2
paula_scheuchel
paula_scheuchel :
From where is the jacket😍
2026-08-16 20:42:45
26
ckampodvall
Подвал Дагестана :
2026-08-16 20:42:54
1
mrrrr322
marta :
обворожительная 🙏❤️
2026-08-16 15:37:35
3
chsdangerous
chsdangerous :
Вы чем то мне напоминаете баби из три метра над уровнем неба, очень красивая
2026-08-16 17:14:27
149
ghost.girl0410
ghost girl :
как называеться такая верхняя одежда, очень красиво🙏🏻😻🔥
2026-08-18 05:55:14
3
veronichkachichilik
veroonichka :
откуда этот шикарный бомбер??
2026-08-16 11:51:04
29
al1nakurmanaeva
𝐴𝑙𝑖𝑛𝑎† :
2026-08-20 21:40:08
0
gucciismyfavitem
🎼 :
😍
2026-08-16 17:51:34
1
sadi_0_1
sadi_0_1 :
😍😍😍
2026-08-16 15:53:56
2
marwaan_12
ძ ᥥ̵ ℋ ᥥ̵ ♭ 𝒾 𓆩♡𓆪 :
😁😁😁
2026-08-17 11:04:58
0
shad.130
👸🏻 :
@Sheri
2026-08-17 19:10:23
0
To see more videos from user @karina_zhamko13, please go to the Tikwm homepage.

Other Videos
Loved watching these when I was pregnant & trying to picture what our son would look like 💭 #fypシ #eyecolor #babyboy #genetics #worldscollide #MomsofTikTok #newmomlife #wasian #mixedcouple #mixedbaby
Loved watching these when I was pregnant & trying to picture what our son would look like 💭 #fypシ #eyecolor #babyboy #genetics #worldscollide #MomsofTikTok #newmomlife #wasian #mixedcouple #mixedbaby
Okay but this too is gorgeous @Glowmode #womenstop #offtheshoulder #fallfashion #TikTokshopbacktoschool #tiktokshopcreatorpicks
Okay but this too is gorgeous @Glowmode #womenstop #offtheshoulder #fallfashion #TikTokshopbacktoschool #tiktokshopcreatorpicks
Thảm lót chân cho dòng xe Feliz 2025 , Feliz Lite , Feliz Neo đã có tại cửa hàng | Xe Hôm Nay | #xehomnay #xehomnayshop #feliz #feliz2025 #felizlite
Thảm lót chân cho dòng xe Feliz 2025 , Feliz Lite , Feliz Neo đã có tại cửa hàng | Xe Hôm Nay | #xehomnay #xehomnayshop #feliz #feliz2025 #felizlite
I ♡ N25🤍 #nightcordat25 #プロセカ #pjsk #fyp #projectsekai
I ♡ N25🤍 #nightcordat25 #プロセカ #pjsk #fyp #projectsekai
You've probably seen a setting called temperature when working with LLM APIs. But what does it actually do? Does a higher temperature make the model more creative? Not exactly. Temperature controls how the model chooses between possible next tokens. It adjusts the probability distribution before selecting the next word. For example, if the model sees: “The capital of France is…?” it might assign probabilities like Paris 92%, Lyon 3%, London 2%, Berlin 1%, and Other 2%. The model isn’t just picking from a dictionary, but producing a probability distribution. Lower temperature sharpens this distribution, making high-probability tokens more dominant and the output more predictable. Higher temperature flattens it, allowing lower-probability tokens to be more likely, resulting in more variation. A temperature of 0 makes generation more deterministic, favoring the highest-probability tokens. However, it doesn’t guarantee identical outputs every time, as other factors can influence the result. Use lower temperatures for structured outputs like code or data extraction, and higher ones for brainstorming or creative tasks. When building with an API, you can set the temperature per request, but regular users usually rely on the product’s default settings. Thanks for reading! 🙌
You've probably seen a setting called temperature when working with LLM APIs. But what does it actually do? Does a higher temperature make the model more creative? Not exactly. Temperature controls how the model chooses between possible next tokens. It adjusts the probability distribution before selecting the next word. For example, if the model sees: “The capital of France is…?” it might assign probabilities like Paris 92%, Lyon 3%, London 2%, Berlin 1%, and Other 2%. The model isn’t just picking from a dictionary, but producing a probability distribution. Lower temperature sharpens this distribution, making high-probability tokens more dominant and the output more predictable. Higher temperature flattens it, allowing lower-probability tokens to be more likely, resulting in more variation. A temperature of 0 makes generation more deterministic, favoring the highest-probability tokens. However, it doesn’t guarantee identical outputs every time, as other factors can influence the result. Use lower temperatures for structured outputs like code or data extraction, and higher ones for brainstorming or creative tasks. When building with an API, you can set the temperature per request, but regular users usually rely on the product’s default settings. Thanks for reading! 🙌

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