AI recommendations, Python code generation and Live chatbot support all powered by a quantized LLM

This course dives deep into Detailed Numerical Example Using Q-LoRA with Full Backpropagation along with Fine-Tuning, Deployment, Quantization, and RAG with LLaMA-3.2-1B in Google Colab. We will work through a comprehensive numerical example, demonstrating every step involved in processing the input 'hi' using Q-LoRA, including full backpropagation calculations. This hands-on approach will provide a thorough understanding of Q-LoRA's inner workings and its applications in deep learning.

• BPE Tokenization and Embeddings: We begin by tokenizing 'hi' using Byte-Pair Encoding and creating its embedding representation.
• Positional Encoding: Understand how positional information is incorporated into the embeddings.
• Q-LoRA in Self-Attention: Explore how Q-LoRA efficiently adapts the self-attention mechanism using low-rank matrices.
• FFN with LoRA: Learn how LoRA is applied to the Feedforward Neural Network for further adaptation.
• Normalization: See how layer normalization is applied for stable training.
• Logits and Loss: Calculate logits and the Categorical Cross-Entropy Loss for our example.
• Complete Backpropagation: We walk through the full backpropagation process, calculating gradients for every step, including through the LoRA components.
• AdamW Optimization: Update model parameters using the AdamW optimizer, demonstrating how it handles weight decay.
• Gradient Clipping: Implement gradient clipping to prevent exploding gradients during training.
• Quantization: Discuss the role of quantization in Q-LoRA for memory efficiency.

This course is ideal for machine learning practitioners, data scientists, and anyone seeking a deep, practical understanding of Q-LoRA and its role in optimizing large language models. A basic understanding of deep learning and linear algebra is recommended.