In Lecture 37 of our Gen AI in Hindi series, Bipin Kumar goes fully hands-on — writing and running real fine-tuning code live using Kaggle's free GPU, the Unsloth library, and the Qwen2.5-0.5B model.
This is where theory becomes practice. If you've been following the series, this is the session you've been waiting for.

💻 What You'll Build in This Lecture:
A Transaction Categorizer — a fine-tuned LLM that classifies any bank transaction (e.g. "DMart ₹850" → shopping, "IRCTC booking" → travel) into 10 categories automatically.

🧠 Topics Covered in This Lecture:
⚙️ Setup & Tools:

Kaggle — free GPU (T4) for fine-tuning, no cost for learning
Google Colab — alternative for smaller experiments
AWS SageMaker — production fine-tuning walkthrough (ML.G4DN instances)
Unsloth — fastest open-source fine-tuning library with standard notebooks

📂 Dataset Preparation:

JSONL format: system + user + assistant template structure
Train/validation split — why unseen data matters (batsman analogy explained!)
How many examples are enough? Starting small and scaling up

🔢 LoRA — Matrix Math Explained Simply:

Full fine-tuning vs LoRA — why we don't change all 0.8B weights
W + ΔW = W + A×B — the core LoRA equation, step by step
Rank r controls trainable params: r=16 → only 6.4M of 568M params trained
8×r matrix A + r×8 matrix B → same output shape, fraction of the cost

⚡ QLoRA:

Combine quantization (4-bit model weights) + LoRA (frozen base)
Massive GPU memory savings — run fine-tuning on consumer hardware

📊 Reading Training Results:

Training loss vs validation loss — both should decrease
Overfitting & underfitting — how to spot and fix them
accuracy ≈ 1 - validation_loss — quick estimation trick
num_epochs, batch_size, learning_rate, lora_rank, lora_alpha — all explained next class


⏭️ Next Lecture (Lecture 38):
👉 Fine Tuning Parameters Deep Dive — epochs, learning rate, precision, recall, overfitting & underfitting — and completing fine tuning end-to-end

📚 Full Course Roadmap:
🐍 Python → 🤖 Generative AI → 🧠 Agentic AI → ☁️ AWS + Real Projects

🛠️ Tools Used This Lecture:

Kaggle → kaggle.com
Unsloth → unsloth.ai
Hugging Face → huggingface.co
AWS SageMaker → aws.amazon.com/sagemaker


💬 Drop your questions in the comments — Bipin replies!
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