Phase 2: Models, Serving & Observability · 35 hours · Python (pipeline) · YAML (configs) · Bash (scripts)

Capstone 07 — End-to-End Fine-Tuning Pipeline (Data to SFT to DPO to Serve)

An 8B model trained on your own data, DPO-aligned on your own preferences, quantized, speculative-decoded, and served at measurable $/1M tokens.

Hiring signal: Can build capstone 07 end to end

Introduction

The 2026 open stack is Axolotl v0.8, TRL 0.15, Unsloth for iteration, GPTQ/AWQ/GGUF for quantization, vLLM 0.7 with EAGLE-3 for serving. The capstone is to run the whole pipeline reproducibly — YAML in, served endpoint out — and publish a model card under the 2026 Model Openness Framework.

Phases exercised: P2 · P3 · P7 · P10 · P11 · P17 · P18

Type: Capstone Languages: Python (pipeline), YAML (configs), Bash (scripts) Prerequisites: Phase 2 (ML), Phase 3 (DL), Phase 7 (transformers), Phase 10 (LLMs from scratch), Phase 11 (LLM engineering), Phase 17 (infrastructure), Phase 18 (safety) Time: 35 hours

Objective

Learning objectives

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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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