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.
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: Earlier lessons in this course
- Time: ~120 minutes
Objective
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, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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