Path	What	When to use
`adapter_model.safetensors` + `adapter_config.json` (root)	LoRA adapter (~194 MB)	`peft.PeftModel.from_pretrained` / Unsloth `FastModel` — pairs with the upstream `unsloth/gemma-4-E2B-it` base
`tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`, `processor_config.json` (root)	Gemma 4 tokenizer + chat template	required for any inference path
`gguf/`	Q4_K_M GGUF (~3.2 GB, 5-shard split) + per-subdir README	llama.cpp / Ollama / wllama (browser WebGPU/WASM)
`mediapipe/`	LiteRT bundle (`model.litertlm`, ~4.95 GB) + sample WAVE prompts/outputs + per-subdir README	MediaPipe LLM Inference (Android, iOS, web)
`report/`	Eval write-up: full run report, r16-vs-r32 head-to-head, overnight reproducibility check — markdown only	Documentation: how the run was evaluated and how it compares to the rank-16 sibling

The previously-published sibling repos Maelstrome/lora-wave-session-r32-{gguf,merged,onnx,onnx-fused,report,mediapipe} have all been consolidated into this repo and deleted. The current gguf/ subdir is a fresh build from a PEFT re-merge (the original unsloth-merged base produced corrupt all-<pad> output and was never trustworthy). Any external link to the old sibling URLs will 404 — update to the appropriate subdir of this repo.

Sibling runs

This is the rank-32 / 1-epoch A100 training of the WAVE corpus. The rank-16 / 3-epoch RTX 5080 sibling lives at Maelstrome/lora-wave-session (same subdir layout: adapter at root, gguf/ subdir). On the same frozen 428-row test split, this rank-32 run is measurably stronger on every probability metric:

	rank-16 (sibling)	rank-32 (this run)
LoRA completion NLL	4.7149	4.5576
LoRA perplexity	111.59	95.35
Paired wins vs base	386 / 428 (90.2%)	428 / 428 (100%)
Mean NLL Δ vs base	0.327 nats	0.508 nats
Sign-test p-value	9.5 × 10⁻⁷¹	2.9 × 10⁻¹²⁹

Full head-to-head in report/COMPARISON.md and report/REPORT.md.

Provenance and intended use

Trained for the WAVE app, a wellness/reflection tool — not a medical device, not clinical decision support, not a substitute for professional advice. Use under the Gemma Terms of Use.

Try it

🌊 Interactive demo: Maelstrome/lora-wave-session-demo — Gradio Space with surface-specific example prompts (backed by the rank-16 sibling; weights swap is a one-line config change).

Quickstart

Browser (wllama, WebGPU/WASM)

import { Wllama } from '@wllama/wllama/esm/index.js';

const wllama = new Wllama({ default: '/wllama.wasm' });
await wllama.loadModelFromHF(
  {
    repo: 'Maelstrome/lora-wave-session-r32',
    file: 'gguf/gemma-4-e2b-it-peft.Q4_K_M-00001-of-00005.gguf',
  },
  { n_ctx: 8192 },
);

wllama follows the 5-shard split automatically from the first shard. See gguf/README.md for full details.

PEFT + Unsloth (CUDA, server-side)

python

from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="Maelstrome/lora-wave-session-r32",  # PEFT auto-loads base
    max_seq_length=4096,
    load_in_4bit=True,
)

Or with vanilla PEFT:

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-E2B-it")
tok = AutoTokenizer.from_pretrained("unsloth/gemma-4-E2B-it")
model = PeftModel.from_pretrained(base, "Maelstrome/lora-wave-session-r32")

Ollama / llama.cpp (GGUF)

bash

# Ollama
ollama create wave-r32 -f - <<EOF
FROM hf://Maelstrome/lora-wave-session-r32/gguf/gemma-4-e2b-it-peft.Q4_K_M-00001-of-00005.gguf
EOF
ollama run wave-r32

# llama-cli
llama-cli -hf Maelstrome/lora-wave-session-r32:gguf/gemma-4-e2b-it-peft.Q4_K_M-00001-of-00005.gguf --jinja

The Q4_K_M is split into 5 ≤512 MB shards; llama.cpp and wllama both auto-discover shards 2–5 from the first. Single-file Ollama / LM Studio imports work the same way.

MediaPipe LLM Inference (Android / iOS / web)

import { FilesetResolver, LlmInference } from "@mediapipe/tasks-genai";

const genai = await FilesetResolver.forGenAiTasks(
  "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-genai/wasm",
);
const llm = await LlmInference.createFromOptions(genai, {
  baseOptions: {
    modelAssetPath:
      "https://huggingface.co/Maelstrome/lora-wave-session-r32/resolve/main/mediapipe/model.litertlm",
  },
  maxTokens: 4096,
  topK: 1,
  temperature: 0,
});
const out = await llm.generateResponse(userPrompt);

See mediapipe/README.md for full Android/iOS instructions and the sample WAVE prompt/output JSONs that ship next to the .litertlm for sanity-checking your wiring.

Example prompts

The model expects a system prompt establishing it as WAVE, plus a per-surface user prompt with <surface>, <patient_context>, and <task> blocks. Output is strict JSON.

`phase_narration` (six-line meditation)

<surface>phase_narration</surface>
<chunk>Number 5 of 5 - Close. Purpose: invite comparison to the start, normalize any outcome, and prepare for a final check-in.</chunk>
<patient_context>{"chunkNumber":5,"matType":"none","medicationStatus":"none","startingIntensityBand":"1-6","trigger":"unknown","usedSubstanceToday":false}</patient_context>
<task>Generate exactly 6 patient-facing narration lines. Return only strict JSON. Schema: {"lines":["...", ...]}</task>

Expected output (use max_new_tokens ≥ 384):

{"lines":["You've made it to the end of this practice.","Check in with your urge now — has anything shifted?","...","...","...","..."]}

`reflection` (post-session card)

<surface>reflection</surface>
<patient_context>{"durationSeconds":780,"endingIntensity":2,"intakeIntensity":7,"matType":"buprenorphine","medicationStatus":"on_time","sessionsCount":12,"trigger":"stress","usedSubstanceToday":false}</patient_context>
<task>Write the post-session reflection card. Return only strict JSON. Schema: {"insight":"...","journalPromptQuestion":"...","nextSteps":{"a":"...","b":"...","c":"...","d":"..."}}</task>

`check_in` (multi-turn)

<surface>check_in</surface>
<specialized_surface>lora-check-in-1</specialized_surface>
<patient_context>{"intakeIntensity":7,"matType":"buprenorphine","trigger":"stress"}</patient_context>
<task>Open turn 1: ask the patient to rate their current urge intensity 1-10. Schema: {"reply":"...","endConversation":null}</task>

Training


Base	`unsloth/gemma-4-E2B-it`
Method	QLoRA (4-bit) via Unsloth `FastModel`
Adapter rank / alpha / dropout	32 / 32 / 0
Target modules	All language + attention + MLP layers (vision/audio frozen)
Trainable parameters	25.3 M
Optimizer	`adamw_8bit`
LR	2e-4, cosine schedule
Warmup	21 steps (~3%)
Weight decay	0.001
Max grad norm	0.3
Batch / grad-accum	1 / 8 (effective 8)
Max sequence length	4096 (preflight max = 2,227, no truncation)
Epochs	1 (428 steps)
Chat template	`gemma-4` (non-thinking, leading `<bos>` stripped)
Response masking	`train_on_responses_only` (Gemma 4 markers)
Hardware	NVIDIA A100 80 GB SXM4 (Thunder Compute)
Backend	Unsloth 2026.5.2 + Torch 2.11.0 + CUDA 13.0

Final training loss: 0.241. Wall clock: ~2h 26m train + ~1h 15m eval.

Evaluation

Held-out completion eval (n=428, full test split)

Metric	Base Gemma 4 E2B	This adapter	Delta
Completion NLL	4.9312	4.5576	−0.374
Completion perplexity	138.55	95.35	−43.20
Paired wins (LoRA assigned higher prob to reference)	—	428 / 428 (100%)	—
Mean per-example NLL Δ	—	0.508 nats	95% bootstrap CI [0.477, 0.537]
Median per-example NLL Δ	—	0.454 nats	—
Sign-test p-value	—	2.89 × 10⁻¹²⁹	overwhelming

Surface coverage on test split: check_in 144, phase_narration 147, reflection 137.

Generation eval (n=60 balanced, LoRA-only, 4bit)

Gate	All 60	check_in (20)	phase_narration (20)	reflection (20)
Style pass	100%	100%	100%	100%
Medical-directive pass	100%	—	—	—
No-markdown / no-analysis-voice	100%	—	—	—
JSON validity (160-tok cap)	75%	100%	25%	100%
JSON validity (384-tok cap on phase)	~95%	100%	85%	100%
Schema pass (384-tok cap on phase)	~90%	90%	80%	100%

Known quirks

Phase narration needs max_new_tokens ≥ 384 — the original 160-token cap truncated the JSON close on most phase prompts. check_in is fine at 96; reflection at 192.
Residual phase JSON-close defect. After raising the phase budget to 384 tokens, 4/20 phase examples still emit "} (missing ]) instead of "]}. A reproducibility re-run on those 4 IDs produced byte-identical outputs (8/8 matched the originals exactly), confirming this is a deterministic learned defect on a small subset of phase prompts — not sampling noise. Recommended fix at inference time: a deterministic JSON-repair pass that detects an unclosed lines array and inserts the missing ]. See Maelstrome/lora-wave-session-r32-report for the full diagnosis and patch.

Dataset

Maelstrome/lora-wave-session-dataset — 4,277 examples across three surfaces, stratified 80/10/10 by splitKey (seed 7).

Status mix: 62% synthetic_draft, 37% draft, 1% ready. No real PHI.

Limitations

Wellness scope only. Do not use for medical diagnosis, crisis triage, or clinical decision support.
Trained mostly on synthetic and draft-status data, not clinician-validated production data.
Outputs are constrained-format JSON. The model is not optimized for open-ended chat.
Training data is English; multilingual behavior was not measured.
Phase narration needs max_new_tokens ≥ 384 and may need a JSON-repair post-process for the ~5% edge cases — see Known quirks.

License

Gemma Terms of Use. See https://ai.google.dev/gemma/terms.

Framework versions

PEFT 0.19.1
Unsloth 2026.5.2
Transformers 5.5.0
Torch 2.11.0+cu130

Maelstrome/lora-wave-session-r32

lora-wave-session-r32

Repository layout