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Huzayfah-Patel/mindbridge-phq9-hindi-LoRA

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Model Card

MindBridge Hindi PHQ-9/GAD-7 — Gemma 4 E2B LoRA Adapter (~80-120 MB)

Unsloth QLoRA adapter for google/gemma-4-E2B-it. For the pre-merged fp16 weights (~5 GB), see the companion repo `Huzayfah-Patel/mindbridge-phq9-hindi-merged`.

Loading the adapter

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("google/gemma-4-E2B-it")
model = PeftModel.from_pretrained(base, "Huzayfah-Patel/mindbridge-phq9-hindi-LoRA")
tok = AutoTokenizer.from_pretrained("google/gemma-4-E2B-it")

Or via Unsloth for QLoRA inference:

python

from unsloth import FastLanguageModel
model, tok = FastLanguageModel.from_pretrained(
    "Huzayfah-Patel/mindbridge-phq9-hindi-LoRA",
    max_seq_length=4096,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Use case

Hindi-first offline PHQ-9 + GAD-7 mental-health screening for India's ~1 million ASHA (Accredited Social Health Activist) community-health workers. Deployed on iPhone via Cactus React Native SDK (INT8-apple variant on Apple Neural Engine + ARM SMMLA CPU acceleration). The fine-tune teaches the base model to emit a Gemma 4 native interpret_response tool call returning {score: int 0-3, rationale_english: str, confidence: float in {0.6, 0.8, 0.95}} given a PHQ-9 or GAD-7 item context plus a patient's Hindi utterance.

Item-9 (suicidality) handling layered on top via a deterministic rule engine in the iOS app — the LLM is one signal in a defense-in-depth pipeline, NOT the sole safety net (per project docs §9 hard-coded safety rules).

Evaluation — hierarchical kill-gate verdict

PASS — fine-tune ships. Measured on a 224-row held-out set (200 stratified random teacher carve-out + 24 hand-authored Item-9 adversarial; 222 rows published in the companion evaluation dataset after removing 2 byte-level leaks identified via utterance-only embedding audit).

Gate	Threshold	Base	Fine-tuned	Verdict
Format (JSON tool-call validity)	≥ 95%	100.0%	100.0%	✓ PASS
Safety (Item-9 sensitivity, 24 adversarial)	≥ 90%	95.8% (23/24)	91.7% (22/24)	✓ PASS (1-case regression vs base — honestly disclosed below)
Utility (Likert accuracy delta vs base, 200 main)	≥ 10pp	62.5%	87.5%	✓ PASS (+25.0pp, way above bar)
Brier score (3-class confidence calibration; informational)	—	0.290	0.125	halved — substantially better-calibrated

Three caveats disclosed for full transparency:

1.Format-rate parser lenience. Phase 0's empirical base-model floor was 76.9% JSON validity (strict harness). The 100% Format rate here uses a lenient regex that tolerates Gemma 4's native unquoted-key tool-call syntax. Both base and fine-tune pass through the same parser, so the kill-gate verdict is apples-to-apples (relative comparison valid). The absolute 100% number should NOT be read as "+23pp parser-gap closure" — most of that gap is parser change, not model improvement.

1.1-case Item-9 regression vs base. Base catches 23/24 adversarials; fine-tune catches 22/24. Fine-tune clears the 90% Safety gate (91.7% > 90%) but at a 1-case cost. Reported because pretending otherwise would be dishonest; ships because (a) the pre-specified gate was cleared without exception, (b) the +25pp Utility lift is substantively large, (c) the production iOS app has a deterministic Item-9 rule engine layered on top of the LLM — the fine-tune's Item-9 sensitivity is one signal in a defense-in-depth pipeline.

1.In-distribution main split. The 200 main rows are a stratified random teacher carve-out (same Gemma 4 26B-A4B MoE teacher pool as training). Embedding-similarity audit on the Hindi utterance text alone (not full prompt) shows 173/200 flagged ≥0.8 cosine to nearest training neighbor (mean 0.882) — expected by design. The 24 adversarial rows are dad-authored single-source (mean cosine 0.770, max 0.911); these drive the Safety-gate measurement and are the cleaner generalization signal.

Hyperparameters (Unsloth QLoRA fine-tune on Colab Pro+ A100 40GB)

Parameter	Value
Base model	`google/gemma-4-E2B-it` (2.3B effective params, dense + PLE, USM audio encoder)
Quantization (training)	4-bit NF4 via bitsandbytes
LoRA rank `r`	16
LoRA `α`	32
LoRA dropout	0.05
Target modules (7)	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Audio encoder modules	FROZEN via `requires_grad=False` (preserves Hindi audio quality per project audio hard-rule)
Learning rate	2e-4
LR scheduler	Cosine
Warmup ratio	0.1
Max grad norm	0.3
Optimizer	`adamw_8bit`
Precision	bf16
Epochs	3
Effective batch size	1 × 4 grad-accum = 4
Random state	3407
Training corpus	2,883 rows (companion `mindbridge-phq9-hindi-dialogues` dataset)
Wall time	~2h00m on A100 40GB
Pilot ablation	Config A (2 epochs constant LR, dropout=0.0) vs Config B (3 epochs cosine LR, dropout=0.05) on 500 examples + 50-row main-only held-out subset; Config B winner by Likert tiebreak (+22pp delta vs A) after both cleared Format ≥95% gate.
Loss	All-token SFT fallback (assistantonlyloss=False) — Gemma 4's chat template lacks `{% generation %}` markers that HuggingFace TRL `return_assistant_tokens_mask=True` requires; suboptimal but workable on templated tool-call task at 2,883 rows.

Authorship

Engineered by Huzayfah Patel — UK-registered psychiatrist + software engineer. Sole author of: training pipeline + hyperparameter selection + Unsloth/Colab infrastructure + iOS Cactus integration + this model card.

The companion datasets (Hindi seeds + audio fixtures) are co-authored with Nazir Patel (native Hindi reader/writer) — see the dataset cards for data-authorship details. This model card is engineering-only attribution.

Limitations

—Not validated for clinical deployment. Multi-clinician inter-rater reliability study + ASHA field testing + CDSCO/DCGI regulatory review required before any India clinical deployment.
—Single-clinician review of all synthetic vignettes (Huzayfah Patel alone) — no multi-rater concordance characterization.
—Synthetic vignettes only. No real patient data. Persona bias rebalanced 1:1:1 across postnatalmother / olderwoman / man personas; underrepresents other demographic groups.
—Item-9 sensitivity regressed by 1 case vs base on 24-row held-out adversarial subset (91.7% vs 95.8% base). Production app handles Item-9 (suicidality) via a deterministic rule engine layered on top of the LLM; the fine-tune is not the sole safety net.
—In-distribution caveat on the 200-row Utility-gate evaluation (200 rows are stratified random carve-out from the same teacher pool as training; the 24 Item-9 adversarials are single-source dad-authored and genuinely held-out).
—Pre-specified marginal-improvement policy enforced (6-9pp Likert delta → drop fine-tune; +25pp here cleared the threshold substantively, not marginally).

Companion datasets

—`Huzayfah-Patel/mindbridge-phq9-hindi-seeds` — 144 hand-authored Hindi seed examples
—`Huzayfah-Patel/mindbridge-phq9-hindi-dialogues` — 2,883 ShareGPT-format training rows (Gemma 4 26B-A4B teacher expansions + routine seeds)
—`Huzayfah-Patel/mindbridge-phq9-hindi-evaluation` — 222-row held-out + eval-results/ charts + verdict JSON
—`Huzayfah-Patel/mindbridge-phq9-hindi-audio-fixtures` — 30 Hindi audio fixtures (16 kHz mono Int16 WAV)

Citation

bibtex

@misc{patel2026mindbridge,
  title  = {MindBridge: Hindi-first PHQ-9/GAD-7 Screening with Gemma 4 E2B},
  author = {Patel, Huzayfah},
  year   = {2026},
  url    = {https://github.com/HP-00/MindBridge-Gemma-4},
  note   = {Gemma 4 Good Hackathon submission}
}

License

CC-BY 4.0

Project context

MindBridge is a Hindi-first offline PHQ-9 + GAD-7 mental-health screening app for India's 1 million ASHA workers, built on Gemma 4 E2B INT8-apple via Cactus React Native on iPhone, fine-tuned via Unsloth QLoRA. Submitted to the Gemma 4 Good Hackathon (deadline 2026-05-18, $200K prize pool). Upstream: https://github.com/HP-00/MindBridge-Gemma-4.