04. Fine-Tuning, LoRA, Multimodal AI, Image & Audio

27. Fine-Tuning

🇬🇧 English Definition

Fine-tuning updates model parameters using a task-specific dataset to adapt model tone, style, output formatting, or specialized domain behavior beyond zero-shot prompting capabilities.

🇮🇳 Hinglish Definition

Fine-tuning model ke underlying neural weights ko update karta hai. Factual knowledge store karne ke bajaye, ye model ki style, tone, format aur specific task execution behavior ko change karne mein sabse effective hai.

// Fine-Tuning Training Loop Pseudocode (PyTorch style)
import torch

def train_epoch(model, dataloader, optimizer):
    model.train()
    total_loss = 0
    for batch in dataloader:
        optimizer.zero_grad()
        outputs = model(input_ids=batch['input_ids'], labels=batch['labels'])
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss / len(dataloader)

print("Training Loop Pseudocode Initialized.")
            

🔍 Line-by-Line Code Breakdown

loss.backward(): Computes gradients of loss function with respect to model trainable weights.
optimizer.step(): Updates neural network parameters to minimize task error.

🎯 Interview Focus & Questions

Q: When should you choose Fine-Tuning over Prompt Engineering or RAG?
A: Choose fine-tuning when you need to enforce a specific complex output structure, customize model tone/style, or reduce latency/token consumption on repeated complex tasks.

28. LoRA and PEFT

🇬🇧 English Definition

Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) freeze base model weights and inject trainable low-rank decomposition matrices, reducing GPU memory by 80%+.

🇮🇳 Hinglish Definition

LoRA (Low-Rank Adaptation) base model ke giant weight matrices (W) ko freeze kar deta hai aur chote low-rank matrices (A aur B) inject karta hai. Isse VRAM usage dramatic reduce hoti hai aur training ultra-fast ho jaati hai.

// Low-Rank Decomposition Weight Update (W = W0 + B * A)
import numpy as np

d, k = 4096, 4096  // Base linear layer dimensions
r = 16            // Low rank factor

// Full Rank Matrix size: 4096 x 4096 = 16,777,216 params
full_params = d * k

// LoRA Low-Rank Matrices: (4096 x 16) + (16 x 4096) = 131,072 params
lora_params = (d * r) + (r * k)

print(f"Original Params: {full_params:,}")
print(f"LoRA Params: {lora_params:,} ({(lora_params/full_params)*100:.2f}% of original)")
            

🔍 Line-by-Line Code Breakdown

d, k, r: Shows how rank r=16 reduces trainable parameters from ~16.7M down to ~131K (99.2% parameter reduction!).

🎯 Interview Focus & Questions

Q: What is QLoRA?
A: QLoRA quantizes the frozen base model to 4-bit NormalFloat (NF4) and adds 16-bit LoRA adapter matrices, allowing fine-tuning of a 65B parameter model on a single 48GB GPU.

29. Multimodal Generative AI

🇬🇧 English Definition

Multimodal AI models process and generate multiple data modalities (text, vision, audio, video) simultaneously by mapping different input encoders into a shared latent space.

🇮🇳 Hinglish Definition

Multimodal AI images, text, audio aur video ko ek sath process kar sakta hai. Vision-language models image + text question input lekar detailed natural language answer provide karte hain.

// Multimodal Prompt Payload Concept (Text + Image)
multimodal_request = {
    "model": "genai-vision-v1",
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is shown in this diagram and what are its key components?"},
                {"type": "image_url", "image_url": "https://example.com/system_architecture.png"}
            ]
        }
    ]
}
print("Multimodal Request Target:", multimodal_request["messages"][0]["content"][0]["text"])
            

🔍 Line-by-Line Code Breakdown

multimodal_request: Payload combining text prompt objects with base64/URL image objects.

🎯 Interview Focus & Questions

Q: How do Vision Transformers (ViT) process images?
A: ViTs break an image into a grid of non-overlapping patches (e.g. 16x16 pixels), flatten patches into 1D vectors, add positional embeddings, and feed them into a standard Transformer encoder.

30. Image Generation

🇬🇧 English Definition

Image generation models (Stable Diffusion, Midjourney, DALL-E) rely on Latent Diffusion models that gradually denoise random Gaussian noise conditioned on text prompt embeddings.

🇮🇳 Hinglish Definition

Image generation models Latent Diffusion process par kaam karte hain. Text prompt embedding noise reduction loop ko guide karti hai step-by-step jab tak high resolution final image na ban jaaye.

// Conceptual Diffusion Denoising Step Simulation
import numpy as np

def denoise_step(latent_noise, text_embedding, timestep):
    // Simulate UNet noise prediction guided by text prompt vector
    predicted_noise = np.sin(latent_noise + timestep) * 0.1
    // Subtract predicted noise
    cleaned_latent = latent_noise - predicted_noise
    return cleaned_latent

initial_noise = np.random.randn(1, 4, 64, 64)
step1 = denoise_step(initial_noise, text_embedding=None, timestep=1000)
print("Denoised Latent Shape:", step1.shape)
            

🔍 Line-by-Line Code Breakdown

denoise_step: Simulates iterative UNet reverse diffusion pass converting random noise into clear structured image latents.

🎯 Interview Focus & Questions

Q: What is the role of the VAE (Variational Autoencoder) in Latent Diffusion?
A: The VAE encoder compresses high-resolution pixel images into lower-dimensional latent space for efficient diffusion training, and the VAE decoder reconstructs latent vectors back into pixel images.

31. Speech and Audio AI

🇬🇧 English Definition

Speech AI includes Automatic Speech Recognition (ASR e.g. Whisper) to transcribe spoken audio into text, and Text-to-Speech (TTS) models to synthesize natural human voice audio.

🇮🇳 Hinglish Definition

Speech AI do main parts mein divided hai: ASR (Whisper) jo spoken voice ko text banata hai, aur TTS jo generated text ko realistic audio output mein synthesize karta hai.

// Speech Pipeline Flow Representation
def audio_pipeline_flow(audio_input):
    // 1. ASR Transcribe Audio -> Text
    transcript = "User said: What is the weather today?"
    // 2. LLM Process Transcript -> Answer Text
    answer_text = "The weather is sunny with 24C."
    // 3. TTS Synthesize Answer Text -> Audio Waveform
    audio_output = f"Synthesizing speech for: '{answer_text}'"
    return transcript, answer_text, audio_output

print(audio_pipeline_flow("sample_mic_input.wav"))
            

🔍 Line-by-Line Code Breakdown

audio_pipeline_flow: End-to-end voice assistant flow connecting ASR -> LLM -> TTS.

🎯 Interview Focus & Questions

Q: What architecture powers OpenAI Whisper?
A: Whisper is an encoder-decoder Transformer trained on 680,000 hours of multilingual and multitask supervised audio data using log-Mel spectrogram inputs.

32. Video Generation Concepts

🇬🇧 English Definition

Video generation models extend 2D diffusion architectures with temporal 3D self-attention mechanisms, ensuring frame-to-frame motion consistency and physical realism.

🇮🇳 Hinglish Definition

Video generation image generation se complex hai kyunki har consecutive frame mein object shape, motion aur lighting consistent rehni chahiye (Temporal Consistency).

// Video Latent Frame Tensor Dimensions
import numpy as np

// [Batch, Frames, Channels, Height, Width]
video_tensor = np.zeros((1, 16, 4, 64, 64))
print(f"Video Tensor Shape (Frames={video_tensor.shape[1]}, Spatial={video_tensor.shape[3]}x{video_tensor.shape[4]}):", video_tensor.shape)
            

🔍 Line-by-Line Code Breakdown

video_tensor: 5D tensor adding time dimension (Frames) to standard spatial latent representations.

🎯 Interview Focus & Questions

Q: What is the primary bottleneck in AI Video Generation?
A: Temporal consistency (preventing flickering across frames), high VRAM memory consumption for 3D attention, and physical motion modeling realism.