# Capstone Project Blurb

Email template for advertising LocoLLM to capstone project students.

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**Subject:** Capstone Project Proposal: LocoLLM — Local AI on a Student Budget

Hi [Name],

I'd like to propose **LocoLLM** as a capstone project option for the upcoming semester. It's an open-source framework where students build task-specific AI models that run entirely on consumer laptops — no cloud APIs, no subscriptions, no expensive hardware.

**The pitch to students:** You'll fine-tune a small language model to become a specialist in a domain you choose (math reasoning, code generation, academic writing, etc.), then prove it beats the general-purpose model with real benchmarks. By the end, your adapter ships as part of a growing ecosystem that future cohorts will build on.

**What makes it a good capstone:**

- **Real research question** — Can a collection of cheap, small specialists match expensive frontier AI? Students contribute evidence toward answering this.
- **Tangible deliverable** — Each team produces a working model adapter with training data, benchmarks, and documentation. It's not a report that sits on a shelf.
- **Accumulative** — Each cohort's work extends the previous one. Students are contributing to something that outlasts the semester.
- **Publication potential** — The project is designed to generate benchmarking data that's currently thin in the literature (quantized small model performance on domain-specific tasks).

**Skills needed (all learnable with AI assistance):**

- **Python** — basic proficiency is enough; the framework, CLI, and scripts are already built
- **Data curation** — selecting, cleaning, and formatting training examples (the most important skill — this is where adapter quality comes from)
- **Critical evaluation** — designing benchmarks, interpreting results, understanding what "better" means statistically
- **Technical writing** — documenting training decisions, writing up results, contributing to project docs

**Students do NOT need:**

- Machine learning or deep learning experience (the training pipeline is turnkey — run one script)
- A GPU (training runs free on Google Colab; we have a one-click notebook in the repo)
- Prior experience with LLMs (the architecture docs explain everything from first principles)

The project repo is at **https://github.com/michael-borck/loco-llm** and the live site is at **https://locollm.org**. Students can open the [training notebook](https://colab.research.google.com/github/michael-borck/loco-llm/blob/main/notebooks/train_math_adapter.ipynb) right now and have a working adapter in 20 minutes.

Happy to chat more about how it fits into the unit structure. Teams of 3-4 work well — one person focuses on data, one on training/evaluation, one on documentation and integration.

Cheers,
[Your name]
