Large Language Models
Part-I: Practical LLM &
Part-II: Theoretical Framework for LLM
Embedded Files
Part-I: Practical LLM
Part-I: Practical LLM
1. RAG Fundamentals
1. RAG Fundamentals
Why It Matters: RAG combines LLMs with retrieval systems to reduce hallucinations and improve factual accuracy.
Introduction to RAG
Content:
Three-Step Workflow: Retrieve → Augment → Generate.
Demo: Build a RAG pipeline using Llama-3 and FAISS for vector search.
Code walkthrough for document chunking, embedding, and query augmentation.
Learning Outcome: Implement a basic RAG system from scratch.
Video : https://youtu.be/DBprEyQBeKQ?si=HeILh01l6SxkWgBs
Code Walkthrough: https://www.quantacosmos.com/2024/06/rag-retrieval-augmented-generation-llm.html
2 Advanced RAG Techniques
2 Advanced RAG Techniques
Why It Matters: Basic RAG struggles with complex queries. These methods add structure to retrieval.
2.1. Graph-Based RAG (GraphRAG)
Part 1: Theory
Content:
Represent documents as knowledge graphs (entities + relationships).
Use graph traversal for context-aware retrieval.
Code Walkthrough: https://www.quantacosmos.com/2024/06/rag-retrieval-augmented-generation-llm.html
Part 2: Implementation
Content:
Offline demo with Llama-3 and NetworkX for graph operations.
Querying subgraphs for precise context extraction.
Video: https://youtu.be/pbhRFZwmOvU?si=7lXozQwyxkacZ4We
Code Walkthrough: https://www.quantacosmos.com/2024/06/rag-retrieval-augmented-generation-llm.html
2.2. Knowledge Hypergraphs.
Content:
Extend graphs to n-ary relationships (e.g., "Company A acquires Company B for $X in Year Y").
Demo: Storing hyperedges in a graph database (e.g., Neo4j).
Video : https://youtu.be/SPt5O3rpHIo?si=VZuPc_y_Pfs5K0_o
Code Walkthrough: https://www.quantacosmos.com/2024/06/knowledge-hyper-graph-with-llm-rag.html
2.3 Zero-Shot & One-Shot RAG
Zero-Shot:
Content: Answer queries without task-specific training (e.g., "Explain quantum physics to a 5-year-old").
Code Walkthrough: https://www.quantacosmos.com/2024/06/zero-shot-llm-rag-with-knowledge-graph.html
One-Shot:
Content: Adapt to custom tasks with a single example (e.g., "Generate a sales email using this template").
Video: https://youtu.be/AusPKVSkvGI?si=OICT124ec2_LRUT8
Code Walkthrough: https://www.quantacosmos.com/2024/06/one-shot-llm-rag-with-knowledge-graph.html
3. Fine-Tuning & Adaptation
3. Fine-Tuning & Adaptation
Customize LLMs for Domain-Specific Tasks
3.1. Parameter-Efficient Fine-Tuning (PEFT)
3.1. Parameter-Efficient Fine-Tuning (PEFT)
Why It Matters: Full fine-tuning is resource-heavy. PEFT methods reduce costs while retaining performance.
3.1.1. LoRA (Low-Rank Adaptation)
Content:
Inject low-rank matrices into transformer layers.
Mathematical intuition behind rank reduction (SVD analogy).
Code Walkthrough: https://www.quantacosmos.com/2024/06/lora-qlora-and-fine-tuning-large.html
3.1.2. QLoRA (Quantized LoRA)
Content:
4-bit quantization + LoRA for memory-efficient training.
Benchmark comparisons: QLoRA vs. LoRA vs. full fine-tuning.
Video: https://youtu.be/24Px6Gr5uiQ?si=VCdldpU84genKJUo
Code Walkthrough: https://www.quantacosmos.com/2024/06/lora-qlora-and-fine-tuning-large.html
3.1.3. DORA (Dynamic Low-Rank Adaptation)
Content:
Automatically adjust the rank of LoRA matrices during training.
When to prefer DORA over static LoRA.
Video: https://youtu.be/PAalu1hKTy4?si=QOr_c1MeR8SHRygA
Code Walkthrough: https://www.quantacosmos.com/2024/07/finetune-large-language-models-with.html
3.2 Full Fine-Tuning Workflows
3.2 Full Fine-Tuning Workflows
For High-Resource Scenarios
Fine-Tuning Llama-3 Locally
Content:
Hardware Setup: GPU/CPU requirements, RAM optimization.
Data preparation: Formatting instruction datasets (e.g., Alpaca-style).
Code: Training loops, checkpointing, and evaluation.
Video: https://www.youtube.com/watch?v=H1x7Y-6B6Y0
Code Walkthrough: https://www.quantacosmos.com/2024/06/fine-tune-pretrained-large-language.html
4. Advanced Applications
4. Advanced Applications
Innovate with Hybrid AI Systems
4.1 Neuro-Symbolic AI with LLMs
4.1 Neuro-Symbolic AI with LLMs
Why It Matters: Combine neural networks’ pattern recognition with symbolic logic’s reasoning.
Algorithmic Trading Case Study
Content:
Symbolic Component: Rule-based market indicators (e.g., moving averages).
Neural Component: LLM analyzing news sentiment.
Fusion: Decision engine balancing both inputs.
Video: https://youtu.be/5qEXCxsV4Og?si=3tenzF8wDtcZQohE
Code Walkthrough: https://www.quantacosmos.com/2025/02/enhancing-algorithmic-trading-with.html
4.2 Quantization for Efficiency
4.2 Quantization for Efficiency
Why It Matters: Deploy LLMs on edge devices (e.g., laptops, phones).
Quantization Basics
Content:
8-bit vs. 4-bit precision tradeoffs.
Tools: GGUF, bitsandbytes, and llama.cpp.
Video: https://youtu.be/yNNNfFiuKAI?si=9fBEj3EXIRw2_52a
5. Tools & Implementation Guides
5. Tools & Implementation Guides
Hands-On Support for Real-World Projects
5.1 Local Llama-3 Deployment
5.1 Local Llama-3 Deployment
Why It Matters: Avoid cloud costs and privacy risks by running models offline.
Step-by-Step Setup
Content:
Downloading Llama-3 weights (via Hugging Face or direct links).
Using llama-cpp-python for CPU inference.
Optimizing inference speed with Metal (Mac) or CUDA (NVIDIA).
Video Guide: https://youtu.be/AaoxeuQD-Sg?si=ijxRbynG2B98nvt3
6. Next-Gen AI - Multi-Agent LLMs and Policy Gradient RL Explained
6. Next-Gen AI - Multi-Agent LLMs and Policy Gradient RL Explained
Contains:
How LLM agents “negotiate” on every patient case
Attention-based neural policy networks making smart choices
Live reward feedback and policy updates (REINFORCE)
A real Python code walkthrough (Groq API, Keras 3, Sentence Transformers)
Insights into how AI agents learn to make decisions with multiple advisors
YouTube Video: https://www.youtube.com/watch?v=YkyoJFmnaco&t=1s
Please find the code at: https://www.quantacosmos.com/2025/07/next-gen-ai-multi-agent-llms-and-policy.html
7. AI vs Human Writing Robust Hybrid LLM Aided Detection
7. AI vs Human Writing Robust Hybrid LLM Aided Detection
Contains:
How semantic, structural, and entropy features reveal AI text
Why LLM meta-classification (self-consistency voting) beats single-method detection
How adversarial tricks try to fool detectors—and how to spot them
YouTube Video Link: https://www.youtube.com/watch?v=V1FsQgVpPqo&t=194s
The working code is available at: https://www.quantacosmos.com/2025/07/ai-vs-human-writing-robust-hybrid-llm.html
Part-II: Theoretical Framework for LLMs
Part-II: Theoretical Framework for LLMs
1. State-Space Models for LLM
1. State-Space Models for LLM
a. Mamba Explained
a. Mamba Explained
Contains:
What is MAMBA?- Understand the motivation and theory behind Maximum-Memory Attention with Multiplicative Bias Architecture.
Core Building Blocks: See how Causal 1D Convolution (local mixing), State Evolution (SSM core), and Multiplicative Bias (gating) fit together.
Deep Learning Architecture: Explore how MAMBA layers are stacked to create powerful, scalable models for text, time series, and beyond.
Complete Example: Follow a step-by-step walk-through with a small matrix example, seeing how each input is transformed as it passes through the model.
Part-1: YouTube Video Link: https://youtu.be/lDa1_k7An7A?si=mZKJheGnUIi-O_Qc
Part-2: YouTube Video Link: https://youtu.be/-zpMjbvN_QE?si=XQcuSi0OGKfK9-Je
2. How Transformer Support Autoregressive Language Model based LLMs.
2. How Transformer Support Autoregressive Language Model based LLMs.
Contains:
A detailed Mathematical Description
YouTube Video Link: https://www.youtube.com/watch?v=KNoW9E-TDU8
3. How to use Long Text Sequences with LLM ?
3. How to use Long Text Sequences with LLM ?
a. Use of Long Text Sequences with LLM’s Trained on Shorter Text Sequences Part-1
a. Use of Long Text Sequences with LLM’s Trained on Shorter Text Sequences Part-1
Contains:
Why Vanilla Transformer Does not Support Long Range Extrapolation.
Basics of the Current Approaches.
YouTube Video Link: https://youtu.be/q2otBk4Wcx8?si=8LMVy5j0S9K2vN_9
b. Use of Long Text Sequences with LLM’s Trained on Shorter, Part-2 (Attention with Linear Biases)
b. Use of Long Text Sequences with LLM’s Trained on Shorter, Part-2 (Attention with Linear Biases)
Contains:
Attention with Linear Biases Algorithm.
Discussions & future Techniques.
YouTube Video Link: https://youtu.be/I04hB_QAjFU?si=9HqMktBBvEucXiUd
c. Use of Long Text Sequences with LLM’s Trained on Shorter Part-3 RoFormer-Rotary Positional Embedding
c. Use of Long Text Sequences with LLM’s Trained on Shorter Part-3 RoFormer-Rotary Positional Embedding
Contains:
Key issues with the traditional Positional Encoding.
RoFormer Algorithm.
Future Research direction.
YouTube Video Link: https://youtu.be/5WhQecvWX7U?si=OYF5XBEdjrSLZGA8
4. Unlocking the Power of LLMs: Why Quantization Matters and How to Master It Effectively
4. Unlocking the Power of LLMs: Why Quantization Matters and How to Master It Effectively
Contains:
Unlocking the Power of LLMs: Why Quantization Matters and How to Master It Effectively
YouTube Video Link: https://youtu.be/yNNNfFiuKAI?si=idKML0y-6R0KwJf4
5. Evolution of Attention Based Techniques.
5. Evolution of Attention Based Techniques.
a. Revise Your Understanding of Self-Attention
a. Revise Your Understanding of Self-Attention
Contains:
Learn How to transition from Input text to [Query, Key, and Value] Vectors and Ultimately, to the final Context Vector.
YouTube Video Link: https://youtu.be/L_bBglaRPfo?si=N7r_JvBzzz2J388D
b. Cross Attention Vs Self Attention
b. Cross Attention Vs Self Attention
Content Summary:
Cross-attention is a mechanism in deep learning, particularly in Transformer models, that allows one sequence of data (query) to attend to another sequence (key-value pairs) dynamically. Unlike self-attention, which models dependencies within the same sequence, cross-attention enables interaction between different modalities, sources, or input types.
YouTube Video Link: https://youtu.be/WfJ8waoakeQ?si=YzfJ75HZDLCcP9iX
c. Sparse Attention Vs Self-Attention
c. Sparse Attention Vs Self-Attention
Content Summary:
Sparse attention is an optimization technique used in Transformer models to reduce computational complexity by selectively attending to a subset of input tokens instead of all tokens. Unlike full self-attention, which scales quadratically with sequence length, sparse attention introduces structured sparsity in the attention matrix, making it more efficient for long sequences.
YouTube Video Link: https://youtu.be/fWto5Ozpjsc?si=YYl-cAIT7P5TnWQ6
d. Gated Self Attention: A Smarter Alternative to Vanilla Attention for NLP & Agentic-AI
d. Gated Self Attention: A Smarter Alternative to Vanilla Attention for NLP & Agentic-AI
Content Summary:
Gated Self-Attention is a mechanism that blends the original input with the attention output using a learnable gate to control how much influence the attention mechanism has. It's used to prevent misleading information from dominating, which is especially useful for long texts, and it enables dynamic trust control over each feature. This approach is beneficial for tasks like sentiment analysis and multi-modal reasoning.
YouTube Video Link: https://youtu.be/MkdFMvrlyIc?si=sE9S63sIxCvcuRml
e. Mixture of Experts (MoE) Explained: The Secret Behind Smarter, Scalable and Agentic-AI
e. Mixture of Experts (MoE) Explained: The Secret Behind Smarter, Scalable and Agentic-AI
Content Summary:
Want to know what makes the latest AI models faster, smarter, and more efficient? The answer is Mixture of Experts (MoE) — a powerful mechanism that routes each input to specialized mini-networks (experts) instead of using one giant model. In this video, we break down MoE with simple examples, explain how it outperforms traditional attention, and show why it's shaping the future of scalable and modular AI — including Agentic LLMs and intelligent assistants.
YouTube Video Link: https://youtu.be/s0gdPL4wZ4Q?si=oa2_xPTNAEBqkKW0
6. From Slow to Superfast- KV Cache vs Paged Cache vs KV-AdaQuant in Transformers
6. From Slow to Superfast- KV Cache vs Paged Cache vs KV-AdaQuant in Transformers
Contains:
In this Tutorial, we dive deep into one of the most powerful innovations behind modern large language models (LLMs) — the KV Cache, and its cutting-edge evolutions: Paged KV Cache and KV-AdaQuant.
YouTube Video Link: https://youtu.be/fslTpSJ0Ops?si=KiIh0BG9o_7qs8Eb
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