---
title: "Chapter 16: Scale Up and Ship"
description: "Understand scale-up options, check the plan, then move base model size, optional Thor/Unsloth rebuild, and Ollama serve — same harness, one URL change."
type: lesson
order: 16
chapter: "05-the-flywheel"
---

# Chapter 16: Scale Up and Ship

## Objective

Once the flywheel turns on 1B: scale the base model, optionally rebuild on CUDA (Jetson Thor / Unsloth), ship via Ollama, and automate `flywheel.py` with an eval-score chart.

---

## Concept: Same harness, bigger engine

1. **Move up the ladder** (4B → 12B…): same Makefile, more layers/longer runs. Baseline table (Ch 12) tells you if it paid off.  
2. **Thor / CUDA:** Unsloth rebuild. Nemotron notes: BF16 (not casual 4-bit QLoRA), pin stack packages, **do not fine-tune router** on MoE variants.  
3. **Ship:** merge → GGUF → Ollama; point harness at new URL — payoff for OpenAI-compatible API in Ch 06.  

### Final deliverable

`flywheel.py`: generate → verify → filter → train → score; append results row; stop when improvement < threshold. Chart: **eval score vs iteration**.

**Exit:** round *n+1* > *n* on held-out for ≥2 consecutive rounds, no teacher — local, fine-tuned, improving on verified experience.

---

## Concept check

**Q1.** Why can scaling the base model reuse the Chapter 06 Makefile with minimal edits?

**Q2.** What is the payoff of standardizing on `/v1/chat/completions` when you move from mlx-lm to Ollama?

<details>
<summary>Concept answer key — attempt first</summary>

## Answer key (concept)

### Q1

**Model answer:** Pipeline is model-name/path parameterized; data→train→serve loop unchanged.

**Pass criteria:** parameterized pipeline / same stages

### Q2

**Model answer:** Harness client unchanged — only the base URL (and maybe model name) changes.

**Pass criteria:** one URL / same client

</details>

---

## Gate: Decide scale target with the scoreboard

Using your Ch 12 table, pick the next base size (or stay) with a one-sentence rationale to your tutor.

---

## Gate: One larger-model train + eval

Run the existing pipeline on the larger model (Mac MLX if it fits; else plan Thor). Compare held-out scores to 1B.

---

## Gate: Ship path (when ready)

Merge → GGUF → `ollama create` / serve → harness URL swap → smoke-test one task.

**Pass:** harness completes a task against the shipped endpoint.

---

## Gate: `flywheel.py` + chart

Automate multi-round loop; produce score-vs-iteration chart.

**Pass:** two automated rounds or a dry-run that writes the results table format.

---

## Check your understanding

**Q3.** Your 1B flywheel works but 12B prompted base wins the table. What is a rational next step?

**Q4.** Name the two course constraints that remain true after ship.

<details>
<summary>Answer key — attempt every question first</summary>

## Answer key

### Q3

**Model answer:** Fine-tune the larger model (or accept prompting) — do not only polish 1B; use the table.

**Pass criteria:** move tune up or accept scale result

### Q4

**Model answer:** Local weights; model is fine-tuned (not only prompted).

**Pass criteria:** local + fine-tuned

</details>

---

[← Chapter 15](/fine-tune-local-agent/05-the-flywheel/15-the-no-weights-complement/) · [Course Home →](/fine-tune-local-agent/)