LLM Architectures & Landscape

In 2026, the AI landscape has shifted from simple chatbots to complex, multi-modal architectures. To build the next generation of intelligent systems, understanding the underlying structure of Large Language Models (LLMs) is no longer optional—it is essential.

In this session, Aditya Chhabra (Founder, CreateBytes) deep-dives into the "engine room" of modern AI. He breaks down the Transformer architecture, explains the strategic differences between Encoder and Decoder models, and explores the rising world of Multi-modal models and Open Source ecosystems.

Key Timestamps:
0:00 — Transformers: The backbone of modern AI understanding
0:52 — Introduction to Aditya Chhabra and the mission of CreateBytes & CBX
1:32 — Architecture Overview: Language understanding vs. Text generation
2:12 — "Attention is All You Need": The 2017 revolution and Blue Scores
2:54 — The 4 Pillars: Embeddings, Positional Encoding, Self-Attention, & Multi-Head Attention
3:22 — Input Embeddings: Converting text into high-dimensional vectors
4:07 — Positional Encoding: How models understand word order and sentence structure
4:56 — Self-Attention Mechanism: Queries, Keys, and Values explained
5:40 — Multi-Head Attention: Specialized "doctors" for different linguistic patterns
6:25 — Choosing the Right Variant: Matching architecture to your business goal
7:14 — Encoder-Decoder Models: The listener-speaker dynamic for translation
8:06 — Encoder-Only Models: How BERT masters classification and sentiment
9:07 — BERT Deep Dive: Masked Language Modeling (MLM) and Next Sentence Prediction
9:54 — Decoder-Only Models: The logic behind GPT and generative AI
10:52 — Comparative Analysis: When to use BERT vs. GPT vs. T5
11:31 — Casual Language Modeling: Training models to predict the next word
12:18 — GPT Architecture: Parallel computation and stack decoder blocks
13:24 — Masked Multi-Head Attention: Preventing the model from "cheating"
14:43 — Large Multi-modal Models (LMMs): Processing images, audio, and video
15:34 — VLM Architecture: Connecting Vision Transformers (ViT) to Language Models
16:24 — The Business Choice: Proprietary (GPT-4) vs. Open Weights (Llama/Mistral)
17:38 — Open Source Spotlight: Exploring Falcon, Mistral, and Dolly 2.0
19:53 — Conclusion: Joining the CBX Community

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CreateBytes — Building production-grade AI systems from open-source principles.

In this video:
Deconstruct the Transformer architecture from the ground up.
Understand the training objectives of BERT (Encoder) vs. GPT (Decoder).
Learn how Multi-modal models bridge the gap between vision and text.
Navigate the trade-offs between proprietary APIs and Open Source deployment.

🔍 Explore Our Ecosystem:
Website → https://createbytes.com
Instagram → https://instagram.com/createbytes
LinkedIn → https://linkedin.com/company/createbytes
X (Twitter) → https://twitter.com/createbytes
Discord → https://discord.com/invite/qYzrfHVFyh

📌 Our Flagship Products:
– CB Vision (Visual Intelligence Engine)
– YugYog.ai (AI Surveillance)
– AltrixLabs (AI for Health, Fitness, Payments)
– VisionGPT (Multimodal CV + LLMs)
– Krigat (AI Fitness & Recovery)

🧠 Want to collaborate or build with us?
Drop us a message at → info@createbytes.com

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Видео LLM Architectures & Landscape канала CreateBytes