AI Engineering/Phase 10/Lesson 14

Open Models: Architecture Walkthroughs

You built a GPT-2 Small from scratch in Lesson 04. Frontier open models in 2026 are the same family with five or six concrete changes. RMSNorm instead of LayerNorm. SwiGLU instead of GELU. RoPE instead of learned positions. GQA or MLA instead of full MHA. Mixture-of-Experts at scale. The math you already know covers 95% of them. This lesson reads Llama 3, DeepSeek-V3, Mixtral, Qwen, and Gemma side by side and names the exact line where each architecture diverges.

Learn 45 min Python (stdlib)

Prerequisites: Phase 10, Lessons 04, 05, 12 (Pre-training, Scaling, Inference)

#Learning Objectives

Read the config.json of Llama 3, Mistral, Mixtral, Gemma 2, Qwen 2.5, and DeepSeek-V3 and explain every field
Name the specific architectural change each model made versus GPT-2 Small and justify it from first principles
Compute parameter count, KV cache size, and activation memory for any open model from its config alone
Pick the right open model for a deployment target given latency, memory, and capability constraints

Full Access lesson

Keep going. The rest of this lesson is one step away.

Unlock all 503 lessons, video, code, quizzes and every future update. Pay in your own currency with Paystack.

See plans Sign in

The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.