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This workshop series is extremely hands on and interactive. A dedicated teaching assistant and I will answer your questions within 24 hrs.

No API keys. No per-token bills. No data leaving your machine.
Everyone has used ChatGPT. Far fewer have run a 70-billion-parameter model on hardware they control, fine-tuned it on their own data, and served it to a team. Over six weeks we build that, end to end. You leave with working code, not slides.

HOW IT WORKS
Each week has two sessions.

  1. Tuesday is concepts — 90 minutes, slides and live demo, nothing to install. Come tired, watch, ask questions.
  2. Thursday is the lab — 90 minutes, hands-on, you build the thing. This is the session that matters most.

The gap between them is deliberate. It gives you a day to sit with the ideas before you type.

WHAT YOU NEED
A laptop with 16 GB of RAM or more. Weeks 1, 2 and 4 run entirely on your own machine.
Weeks 3 and 5 need a GPU, and we'll walk you through spinning one up in the cloud. The free tier of Google Colab covers most of it; if you'd rather rent something faster, expect to spend under $15 for the whole course. Notebooks go out before each lab, so you're not configuring anything from scratch during class.
No hardware purchase required.

WHO THIS IS FOR
You're comfortable in a terminal and can read Python. You don't need machine learning experience — we build the concepts as we go — but you should be able to install things and debug them when they break.
If you're looking for a lecture series, this isn't it.

WHAT YOU'LL WALK AWAY WITH

  • Three or more models running locally, and the judgment to choose between them
  • A sizing method that tells you before downloading whether a model fits your hardware
  • A serving endpoint your existing OpenAI-compatible code can hit by changing one line
  • A working RAG pipeline over your own private documents
  • A fine-tuned adapter trained on your own data
  • An evaluation harness, so you can prove a change actually helped

SCHEDULE
All sessions 6:30 PM ET. Tuesdays 90 minutes, Thursdays 2 hours.
Week 1 — Get it running

  • Tue Jan 12 · What "local" actually means, and the model landscape
  • Thu Jan 14 · Three models running on your laptop. Measure latency and quality yourself, and find where "bigger is better" stops being true.

Week 2 — The memory math

  • Tue Jan 19 · Quantization, the memory formula, and the KV cache everyone forgets
  • Thu Jan 21 · Build a sizing worksheet: given your hardware, what runs, at what context length, at what quality.

Week 3 — Serving it properly

  • Tue Jan 26 · Batching, throughput vs. latency, and an honest cost model
  • Thu Jan 28 · Stand up a serving endpoint your existing code can hit by changing one line.

Week 4 — RAG on your own documents

  • Tue Feb 2 · Embeddings, chunking, retrieval, and why retrieval quality caps answer quality
  • Thu Feb 4 · Build a RAG pipeline over your own files. Then break it on purpose and diagnose why.

Week 5 — Fine-tuning with LoRA

  • Tue Feb 9 · LoRA, QLoRA, and the harder question of when not to fine-tune at all
  • Thu Feb 11 · Train an adapter on your own data. Compare before and after on held-out examples.

Week 6 — Evaluation and production

  • Tue Feb 16 · Test sets, LLM-as-judge, guardrails, prompt injection, model lifecycle
  • Thu Feb 18 · Build an eval harness you can run against everything from the previous five weeks.

The syllabus and course link will be shared with you once you've registered.

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