Lessons and code
All 19 lessons, in order. Each one is a step of the course: open it from the Day and Step column, or follow the course from the start.
| # | day · step | folder | what you do | runs on |
|---|---|---|---|---|
| 00 | Day 1 · Step 2 | 00-setup |
install Ollama, llama.cpp, MLX; Fireworks guardrails | Mac |
| 01 | Day 1 · Step 3 | 01-latency-harness |
measure TTFT and ITL; the harness every later lesson reuses | Mac → FW |
| 02 | Day 2 · Step 1 | 02-three-local-servers |
one model served by Ollama, llama.cpp and MLX | Mac |
| 03 | Day 3 · Step 1 | 03-concurrency-sweep |
the throughput vs TTFT curve that sizes a deployment | Mac |
| 04 | Day 4 · Step 1 | 04-quantization |
Q8 vs Q4 vs Q3: speed, ceiling maths, quality check | Mac |
| 05 | Day 4 · Step 2 | 05-kv-cache |
KV cache maths, then break and fix long context | Mac |
| 06 | Day 5 · Step 1 | 06-prefix-caching |
shared prefixes: the ~20× TTFT win, and the anti-pattern | Mac → FW |
| 07 | Day 6 · Step 1 | 07-structured-output |
JSON validity: prompt vs json_object vs json_schema | Mac → FW |
| 08 | Day 6 · Step 2 | 08-batch-api |
evals at half price with the Batch API | FW |
| 09 | Day 7 · Step 1 | 09-eval-harness |
quality + p95 + $/1k tasks in one table, LLM judge | Mac → FW |
| 10 | Day 7 · Step 2 | 10-lora-fireworks |
a real LoRA fine-tune: train → deploy → eval → delete | FW $ |
| 11 | Day 8 · Step 1 | 11-lora-local-mlx |
the same LoRA on your Mac (QLoRA) | Mac |
| 12 | Day 9 · Step 1 | 12-moe-vs-dense |
infer MoE active parameters from decode speed | Mac |
| 13 | Day 9 · Step 2 | 13-speculative-decoding |
draft models, acceptance rate, when it hurts | Mac → FW $ |
| 14 | Day 9 · Step 3 | 14-spark-track |
vLLM and SGLang on the DGX Spark, same harness | Spark |
| 15 | Day 10 · Step 1 | 15-capstone |
build the customer sizing memo from your results | anywhere |
| 16 | Day 11 · Step 1, Day 12 · Step 1 | 16-training-toy |
full vs LoRA, SFT → reward model → RLHF vs DPO, in numpy | anywhere |
| 17 | Day 11 · Step 2 | 17-full-vs-peft-mlx |
full vs LoRA vs DoRA on a real model; data checker | Mac |
| 18 | Day 12 · Step 2 | 18-preference-dpo |
DPO on your Mac (and optionally Fireworks) | Mac → FW |
Every lesson folder has a README.md with the same parts: what and why, the code to read first,
commands to run, expected output, self-check questions and a short explanation of the result.