# Build Small — submission mapping

How OffGridSchedula lines up with every requirement, track, sponsor prize, and badge of the
[Build Small hackathon](https://huggingface.co/build-small-hackathon)
([field guide](https://huggingface.co/spaces/build-small-hackathon/field-guide)).
Tags claimed in the README frontmatter use the field guide's namespaced taxonomy
(`track:*`, `sponsor:*`, `achievement:*`).

## Hard rules

| # | Rule | Status | Evidence |
|---|------|--------|----------|
| 1 | Every model under 32B parameters | ✅ | Two local models, both far under the cap: extraction is [`gemma-cal` E4B](https://huggingface.co/build-small-hackathon/gemma-4-cal-gguf) (~4B effective params, ~5 GB GGUF at Q4) and planning is [`openbmb/MiniCPM5-1B`](https://huggingface.co/openbmb/MiniCPM5-1B-GGUF) (1B). |
| 2 | Gradio app, hosted as an HF Space (Docker OK) | ✅ | [`app.py`](../app.py) is a Gradio Blocks app served from a Docker SDK Space running llama.cpp, now hosted in the hackathon org: [`build-small-hackathon/OffGridSchedula`](https://huggingface.co/spaces/build-small-hackathon/OffGridSchedula). |
| 3 | Demo video | ✅ | Recorded and linked from the README: [youtu.be/m-o0u9X3tI4](https://youtu.be/m-o0u9X3tI4) (storyboard in [`docs/demo-script.md`](./demo-script.md)). |
| 4 | Social media post, linked from the README | ✅ | Published and linked from the README: [X (1)](https://x.com/nate_mauer/status/2064920352845709419), [X (2)](https://x.com/nate_mauer/status/2065661878441750916), and [LinkedIn](https://www.linkedin.com/feed/update/urn:li:ugcPost:7471440639969132545) (drafts in [`docs/social-post.md`](./social-post.md)). |
| 5 | ≤ 10 ZeroGPU apps per user | ✅ n/a | Runs on cpu-basic (stub preview) or a dedicated T4 — no ZeroGPU dependency. |
| 6 | README frontmatter tags + short write-up of idea & tech | ✅ | Namespaced tags + the idea-and-tech write-up are in [`README.md`](../README.md). |

## Track — `track:backyard` (Backyard AI)

A specific real person: a busy parent whose kid's school and activity events are buried in a
noisy class group chat. They paste the chat (or a flyer screenshot) from their phone's browser
and get back events, a conflict check against their own calendar, and a ready-to-send reply —
reviewed before anything is saved, exported as a local `.ics` (Apple/Google Calendar one tap
away). Short pasted chats and screenshots are exactly the workload a small local model handles
well — an honest fit, not a stretch.

## Sponsor prize — `sponsor:modal` (Best Use of Modal)

Modal powered the **development** of the platform's model end-to-end:

- [`training/modal_train.py`](../training/modal_train.py) — full QLoRA fine-tune on serverless A100/H100s (dataset → train → GGUF export → HF publish), with persistent Volumes caching base weights and outputs across runs.
- [`training/modal_eval.py`](../training/modal_eval.py) / [`modal_quant_eval.py`](../training/modal_quant_eval.py) — the 60-example task eval served on llama.cpp inside Modal, including an on-volume quantization study (f16 / Q8_0 / Q4_K_M).
- [`training/gated_retrain.py`](../training/gated_retrain.py) — the eval-gated pipeline: train → staging upload → eval → promote **only if it beats the gate**. It rejected eight regressed models before the published one; every one of those runs was a Modal job.

## Sponsor prize — `sponsor:openbmb` (Best MiniCPM Build)

Clicking **Run the agents** invokes **OpenBMB MiniCPM** as the planner (`openbmb/MiniCPM5-1B-GGUF`;
the larger `MiniCPM4.1-8B` variant is a config switch) on a second local llama.cpp instance. It
drives this Space's own MCP tools (`extract_events` → `check_conflicts` → `make_ics`) as a visible
multi-step agent ([`server/orchestrator.py`](../server/orchestrator.py)) — MiniCPM is core to the
agent experience, not a garnish (a deterministic scripted plan is the fallback when the planner
isn't configured). Also the natural evidence for the judged **Best Agent** award.

## Achievement badges (self-declared, all claimed)

| Tag | Badge | Evidence |
|-----|-------|----------|
| `achievement:offgrid` | Off the Grid | All inference runs inside the Space via llama.cpp — no cloud AI APIs. The only optional outbound call is the user's own Google Calendar push. |
| `achievement:welltuned` | Well-Tuned | [`build-small-hackathon/gemma-4-cal-gguf`](https://huggingface.co/build-small-hackathon/gemma-4-cal-gguf) — our published QLoRA fine-tune of Gemma-4 E4B **is the model production serves**, shipped through the eval gate with the [honest scorecard public](./eval-roadmap.md). |
| `achievement:offbrand` | Off-Brand | Custom landing page, grouped nav, dark hero + carousel, elevated tool card, bespoke CSS/JS ([`ui/blocks.py`](../ui/blocks.py), [`static/app.css`](../static/app.css)) — far past the stock Gradio look. |
| `achievement:llama` | Llama Champion | The official `ghcr.io/ggml-org/llama.cpp` server image runs the GGUF + vision mmproj ([`Dockerfile`](../Dockerfile), [`scripts/start_space.sh`](../scripts/start_space.sh)). |
| `achievement:sharing` | Sharing is Caring | Redacted agent traces published to the public dataset [`ParetoOptimal/offgridschedula-traces`](https://huggingface.co/datasets/ParetoOptimal/offgridschedula-traces) — one-click from the Activity tab, or [`training/share_trace.py`](../training/share_trace.py). |
| `achievement:fieldnotes` | Field Notes | [`FIELD_NOTES.md`](../FIELD_NOTES.md) (build retrospective) + [`docs/blog-eval-gated-finetuning.md`](./blog-eval-gated-finetuning.md) (fine-tuning post-mortem) + the [published project blog](https://huggingface.co/blog/build-small-hackathon/offgridschedula) ([source](./blog-offgridschedula.md)). |

Sponsor prizes **not** claimed: OpenAI Codex (no Codex-attributed commits) and NVIDIA Nemotron
(different model family). The cash bonus badges (Off Brand, Tiny Titan, Best Demo, Best Agent,
Bonus Quest Champion, Judges' Wildcard) are judged across all submissions and take no tags.

## Status

All six hard rules are met — nothing outstanding:

- The Space is live in the hackathon org: [`build-small-hackathon/OffGridSchedula`](https://huggingface.co/spaces/build-small-hackathon/OffGridSchedula).
- The model is published at [`build-small-hackathon/gemma-4-cal-gguf`](https://huggingface.co/build-small-hackathon/gemma-4-cal-gguf) (the planner is [`openbmb/MiniCPM5-1B-GGUF`](https://huggingface.co/openbmb/MiniCPM5-1B-GGUF)).
- The [demo video](https://youtu.be/m-o0u9X3tI4) and the social posts (X + LinkedIn) are published and linked from the README.
- The write-up is live as a Hugging Face blog post: [build-small-hackathon/offgridschedula](https://huggingface.co/blog/build-small-hackathon/offgridschedula).