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Lesson · Hinglish · ⏱ 7 min padhne mein

🎛️ Fine-tuning aur LoRA: model ko specialise karna

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🏠 Asli zindagi ki misaal

MBBS doctor general medicine pehle se jaanta hai. Cardiologist banne ke liye woh school dobara nahi karta. Woh kuch focused saal dil pe lagaata hai. Fine-tuning bhi aisa hi hai: aisa model lo jo language pehle se jaanta hai, aur use aapke specific task pe thoda aur train karo. LoRA aisa hai jaise poori textbooks dobara likhne ki jagah compact notes se cardiology seekhna.

💡 Aasaan shabdon mein

Bade model ko zero se pre-train karna crore mein padta hai. Fine-tuning ek pre-trained open model (Llama, Mistral, Qwen) se shuru karke aapke examples pe training aage badhaata hai, jaise support tickets aur unke ideal replies. Full fine-tuning har weight update karta hai, jo mehenga hai. LoRA aur doosre PEFT (parameter-efficient fine-tuning) methods original weights ko freeze karke chhote add-on matrices train karte hain, isliye ek single GPU pe fine-tune ho jaata hai. QLoRA base model ko compress (quantise) bhi karta hai. Hamesha pehle prompting aur RAG se compare karo, kyunki woh saste hain aur aksar kaafi hote hain.

🪜 Step by step

  1. Pehle prompting aur RAG try karo. Fine tune tabhi karo jab aisa consistent style, format ya behaviour chahiye jo woh na de sakein.
  2. Input aur ideal output ke kuch sau se kuch hazaar high quality examples ikattha karo.
  3. Base model chuno aur LoRA use karo, taaki poore model ke bajaye chhote adapter weights train hon.
  4. Kuch epochs train karo, overfitting se bachne ke liye validation loss dekhte raho.
  5. Use karne se pehle apne eval set pe base model ke against evaluate karo. Adapter ko versioned rakho.

🔑 Zaroori shabd

Pre-trained model
Model jo pehle se bahut bade general data pe train ho chuka hai.
LoRA / PEFT
Poore model ki jagah chhote add-on weights train karna.
Quantisation
Memory bachaane ke liye weights ko kam bits mein store karna.

👀 Example dekho

python
from peft import LoraConfig, get_peft_model
config = LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"])
model = get_peft_model(base_model, config)
model.print_trainable_parameters()
# trainable: 0.1% of all parameters → fits on one GPU

Weights ka sirf ek bahut chhota hissa train hota hai.

⚠️ Aam galtiyaan jinse bachna hai

  • Facts jodne ke liye fine tuning. Knowledge ke liye yeh unreliable hai; uske liye RAG behtar hai.
  • Low quality ya inconsistent training examples. Model aapki galtiyaan bhi wafaadaari se copy karta hai.

✅ Jaldi se check karo

Aap chahte ho ki model company ke latest policy documents se jawaab de, jo har hafte badalte hain. Pehle kya try karoge?

  1. Har hafte fine-tune
  2. RAG: sawaal ke time documents retrieve karo
  3. Naya model pre-train karo
Jawaab dekho

B. RAG: sawaal ke time documents retrieve karo: Fine-tuning style aur skills sikhaata hai. Naye, badalte facts ke liye RAG sasta hai aur up-to-date rehta hai.

🙋 Seekhne wale yeh poochhte hain

Fine tuning kitne ki padti hai?

Chhote open model pe LoRA ke saath yeh ek GPU pe kuch ghanton mein ho sakti hai. Hosted fine tuning APIs training token ke hisaab se charge karti hain.

QLoRA kya hai?

4 bit precision mein stored base model pe LoRA, jo memory kam karta hai taaki bade models chhote GPUs pe fine tune ho sakein.

📌 Yaad rakho

Cost ke order mein: prompting → RAG → fine-tuning.

📖 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:

AI Research Engineer

Course mein seekho