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@goonski.devn: Orang kitty eats orang cheetos #orangecat #cheetos #sora #ai #fyp

Goonski
Goonski
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Tuesday 04 November 2025 06:02:35 GMT
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gl7d3
Jolly Gl1d3 :
Waaaaaaaaaaaaaaa, gimmie mod!!!! Waaaaaaaaaaaaaaaaaaa
2025-11-04 14:23:26
0
ryleegrmes
rylee :
wait you’re so right
2025-11-06 16:23:36
1
cutie..oranges
dani minion :
havent posted in 5 months so when you post you decide to post this..
2025-11-04 19:15:38
0
imjustthetoast
Toast :
AI slop
2025-11-04 16:03:25
0
besorth
Besorth :
devvy wevvy has become ai💔
2025-11-04 07:07:51
0
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How ChatGPT work technically? Large Language Models (LLMs) like GPT are sophisticated systems designed to process and generate language, comprised of several key components: Training Data: The extensive collection of text (books, articles, websites) from which the model learns language patterns, styles, and context. Tokenization: Breaking down text into smaller pieces (tokens) for the model to process. It’s like preparing ingredients for cooking. Embeddings: Converting tokens into numerical values to help the model understand word meanings and relationships. Think of it as giving each word a unique numerical fingerprint. Transformer Architecture: The core technology that allows the model to understand context and generate text. It includes: Attention Mechanism: Helps the model focus on relevant parts of the text to produce coherent and context-aware responses. Pre-training and Fine-tuning: Pre-training: The broad learning phase where the model learns from its large dataset. Fine-tuning: Adjusting the model for specific tasks by training it on more targeted data. Output Layer: The component that translates the model's processed information back into human-readable text, whether it's answering questions, completing prompts, or creating content. Decoding Strategies: Techniques the model uses to select the most appropriate words for generating coherent and relevant text. #Tech #education #stem #ai #artificialintelligence #chatgpt
How ChatGPT work technically? Large Language Models (LLMs) like GPT are sophisticated systems designed to process and generate language, comprised of several key components: Training Data: The extensive collection of text (books, articles, websites) from which the model learns language patterns, styles, and context. Tokenization: Breaking down text into smaller pieces (tokens) for the model to process. It’s like preparing ingredients for cooking. Embeddings: Converting tokens into numerical values to help the model understand word meanings and relationships. Think of it as giving each word a unique numerical fingerprint. Transformer Architecture: The core technology that allows the model to understand context and generate text. It includes: Attention Mechanism: Helps the model focus on relevant parts of the text to produce coherent and context-aware responses. Pre-training and Fine-tuning: Pre-training: The broad learning phase where the model learns from its large dataset. Fine-tuning: Adjusting the model for specific tasks by training it on more targeted data. Output Layer: The component that translates the model's processed information back into human-readable text, whether it's answering questions, completing prompts, or creating content. Decoding Strategies: Techniques the model uses to select the most appropriate words for generating coherent and relevant text. #Tech #education #stem #ai #artificialintelligence #chatgpt
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Shift Knob Bride Black Edition🤝🏼 #shiftknob #tuaspersneling #shiftknobbride
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Sống chung với mèo – thử thách kiên nhẫn cấp độ thần thánh Tôi từng nghĩ mèo là loài nhẹ nhàng, thanh lịch. #momatraxem #pet #hottrend #cliptrieuview #dongvat
Sống chung với mèo – thử thách kiên nhẫn cấp độ thần thánh Tôi từng nghĩ mèo là loài nhẹ nhàng, thanh lịch. #momatraxem #pet #hottrend #cliptrieuview #dongvat

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