A 1.08B model fine-tuned to read a family caregiver's daily free-text note about a parent with Parkinson's disease and return structured symptom fields as JSON. Built for the Build Small Hackathon and designed to run fully locally via llama.cpp / GGUF — the whole point is that health data logged at home never leaves the device.

▶️ Try it live: the What Changed app on Spaces — daily logging (typed or spoken), trend charts, and a one-page doctor report, all running this model on-device.

Not medical advice / not a medical device. It organizes a caregiver's own day-to-day observations into a structured form to share with a clinician. It does not diagnose, predict, or recommend treatment.

What it does

Input — a short caregiver note:

"Mom moved with more struggle today, froze up twice, and her meds wore off before lunch."

Output — compact JSON over a fixed schema:

{"mobility": 2, "freezing_episodes": 2, "off_episodes": 1}

Six daily ratings, 1–5 where 5 = best/healthiest: mobility, tremor, stiffness, mood, sleep, alertness.
Six event counts: off_episodes, dyskinesia_spells, freezing_episodes, falls, missed_or_late_meds, hallucinations.

Only fields the note actually mentions appear. The schema is aligned with the Hauser PD Home Diary (ON/OFF + dyskinesia) and MDS-UPDRS motor/non-motor items. The model does exactly one narrow job (note → JSON); all trend detection is deterministic Python downstream, so the model never touches the reasoning that drives the report.

Results — field-F1 (exact / within-±1)

Eval set	exact	tol (±1)
In-distribution (200 held-out)	0.97	0.99
Out-of-distribution probe (25 deliberately-adversarial styles)	0.83	0.91
Base MiniCPM5-1B, zero-shot	~0.00	~0.08

The base model can't do the task untrained; the capability is entirely from fine-tuning. (±1 calibration on a 1–5 scale is subjective and irrelevant to trend detection, so the tol column is the one to trust.)

Training

Method: LoRA (r=16, α=16) via Unsloth, bf16, 2 epochs, ~80 s on a single A10G. Same RAW-completion prompt/parser at train, eval, and serve.
Data: 1,296 synthetic note → label pairs, generated label-first (sample a ground-truth label, then have a teacher write a faithful caregiver note → labels correct by construction). Teacher: Claude Sonnet 4.6, under a strict third-person diary contract, plus a style-diverse robustness top-up (negation, terse/txt, slang, idioms).

Running locally

Quantizations — field-F1 measured through llama.cpp on 80 held-out notes (the real serving path):

file	size	exact	tol (±1)
`MiniCPM5-1B.Q8_0.gguf` ✅ recommended	1.15 GB	0.95	0.99
`MiniCPM5-1B.Q4_K_M.gguf` smaller	0.69 GB	0.90	0.98

Q8_0 is the pick — it matches full precision (F16) at half the size. Q4_K_M trades ~5 points of exact-match for ~0.46 GB if you need it on a very constrained device.

bash

# llama.cpp CLI
llama-cli -m MiniCPM5-1B.Q8_0.gguf -n 96 -p "<build_extract_prompt(note)>"

python

# llama-cpp-python — RAW completion, matches training/eval/serve
from llama_cpp import Llama
llm = Llama(model_path="MiniCPM5-1B.Q8_0.gguf", n_ctx=1024)
out = llm.create_completion(build_extract_prompt(note), max_tokens=96, temperature=0.0)

Recommended: constrain decoding with the project's GBNF grammar so output is always valid schema JSON (correct keys, integer 1–5 / counts) — this also fixes rare out-of-schema event slang at decode time.

Prompt format

RAW completion (no chat template): the app's build_extract_prompt(note) is fed directly and the model emits the JSON object. The exact template + parser live in the project repo.

Limitations

Trained on synthetic data and never validated on real caregiver notes (real notes are exactly the PII the project refuses to collect). The OOD probe is the best available proxy.
Very abbreviated or heavily typo'd phrasings are the weakest spot; out-of-schema event slang is handled at serve time by the GBNF grammar, not the weights.
English only.

zeon01/what-changed-1b

What Changed — Parkinson's caregiver-note extractor (fine-tuned MiniCPM5-1B)