what-changed-1b

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

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)

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):

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.

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

# 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.