Phase 4: Training LLMs · ~120 minutes · Python

Building a Complete LLM Pipeline

Everything from Lessons 01 to 12 is one stage of one pipeline. This lesson is the scaffold that turns those stages into a single end-to-end run: tokenize, pre-train, scale, SFT, align, evaluate, quantize, serve. You will not train a 70B model on a laptop. You will produce the orchestration layer, the manifest, the eval gate, and the rollback plan that a 2026 frontier team uses to decide what gets shipped. This is the capstone.

Hiring signal: Understanding of building a complete llm pipeline internals

What you will learn

Compose the eleven prior lessons (tokenizer, data, pre-training, scaling, SFT, RLHF, DPO, CAI, eval, quantization, inference) into a single reproducible pipeline spec
Define the artifact contract between stages: what each stage consumes, what it produces, and how the next stage verifies the input
Build an orchestrator that tracks experiments, hashes artifacts, and gates ship decisions on eval thresholds
Design the rollback plan: which artifacts are cheap to re-run, which are expensive, and what a corrupted checkpoint costs

Introduction

Type: Build Languages: Python (stdlib) Prerequisites: All Phase 10 lessons 01-12 Time: ~120 minutes

Learning Objectives

Compose the eleven prior lessons (tokenizer, data, pre-training, scaling, SFT, RLHF, DPO, CAI, eval, quantization, inference) into a single reproducible pipeline spec
Define the artifact contract between stages: what each stage consumes, what it produces, and how the next stage verifies the input
Build an orchestrator that tracks experiments, hashes artifacts, and gates ship decisions on eval thresholds
Design the rollback plan: which artifacts are cheap to re-run, which are expensive, and what a corrupted checkpoint costs

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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