Lesson · Hinglish · ⏱ 8 min padhne mein

🔦 Transformers aur attention: LLMs kaise padhte hain

Q: Attention transformer ki kya madad karta hai?

Har word samajhne ke liye doosre words ka context use karna. Attention relevant words ki information milaata hai, taaki har word ka matlab context pe depend kare.

🏠 Asli zindagi ki misaal

Padho: 'Trophy suitcase mein fit nahi hui kyunki woh bahut badi thi.' 'Woh' kise bol rahe hain? Aap apne aap 'trophy' ki taraf wapas dekhte ho. Attention model ko yahi karne deta hai: har word ke liye woh decide karta hai ki baaki kin words pe focus karna hai. Transformer is attention ki bahut saari layers se bana hai.

💡 Aasaan shabdon mein

Text pehle tokens (words ke tukde) mein toota jaata hai, aur har token ek vector (embedding) ban jaata hai. Har transformer layer mein self-attention har token ko baaki saare tokens se information lene deta hai, relevance ke hisaab se weighted, taaki matlab context pe depend kare (nadi ka 'bank' vs bank account). Darjanon layers stack karne se gehri samajh banti hai. Large language models (GPT, Claude, Llama) agla token predict karne ke liye train kiye gaye vishaal transformers hain. Modern image, speech aur code models bhi transformers pe chalte hain.

🎬 Dekho kaise kaam karta hai

Jab model 'it' padhta hai, attention use baaki har word se jodta hai. Sabse strong link 'trophy' se hai, isliye model samajhta hai ki 'it' ka matlab trophy hai.

🪜 Step by step

Text ko tokens mein todo aur har token ko vector (embedding) banao, saath mein uski position ki jaankari.
Self attention: har token khud ko baaki har token se compare karta hai (query vs keys) yeh tay karne ke liye ki uske matlab ke liye kaunse zaroori hain.
Phir zaroori tokens ki information (values) mila leta hai. 'River' ke paas wala 'bank' 'loan' ke paas wale 'bank' se alag ban jaata hai.
Aisi bahut saari layers stack karo, har ek mein attention aur chhota feed forward network, residuals aur normalisation se jude.
LLM ke liye aakhri layer agla token predict karti hai. Text generate karna yahi ek ek token karke repeat karta hai.

🔑 Zaroori shabd

Token: Text ka tukda, English mein average ek word ka lagbhag ¾.
Self-attention: Har token tay karta hai ki baaki har token kitna relevant hai.
Context: Aas-paas ka text jo word ka matlab tay karta hai.

👀 Example dekho

text

Sentence: "I deposited cash at the bank"
Attention for 'bank' → strongest links: 'deposited' (0.41), 'cash' (0.33)
→ model understands: bank = financial institution, not a river bank

Attention weights (example numbers) dikhaate hain ki kin words ne matlab tay kiya.

⚠️ Aam galtiyaan jinse bachna hai

Yeh sochna ki attention ka kharch chhota hai. Yeh text ki lambai ke square ke saath badhta hai, isliye lambe contexts mehenge hain.
Training (weights seekhna) aur prompting (trained model use karna) ko mix karna. Zyaadatar product kaam doosra wala hai.

✅ Jaldi se check karo

Attention transformer ki kya madad karta hai?

Disk pe files store karna
Har word samajhne ke liye doosre words ka context use karna
Training data chhota karna

Jawaab dekho

B. Har word samajhne ke liye doosre words ka context use karna: Attention relevant words ki information milaata hai, taaki har word ka matlab context pe depend kare.

🙋 Seekhne wale yeh poochhte hain

Multi head attention kya hai?

Kai attention mechanisms ek saath, har ek alag rishton pe dhyaan de sakta hai, jaise ek head grammar pe aur doosra matlab pe.

Kya transformers sirf text ke liye hain?

Nahi. Image, audio, video aur multimodal models bhi inhi se chalte hain, un inputs ko bhi tokens mein badal kar.

📌 Yaad rakho

Tokens → vectors → bahut saari attention layers → agle token ki prediction.

📖 Is lesson ke shabd (AI dictionary)

Poora course, aapke roadmap ke hisaab se

Progress tracking, quick checks aur step-by-step order ke saath. Yeh lesson in courses ka hissa hai:

ML Engineer · AI Research Engineer

Course mein seekho