---
license: gemma
base_model: unsloth/gemma-4-E2B-it
library_name: peft
tags:
- gemma
- gemma-4
- lora
- peft
- unsloth
- clinical
- wellness
- structured-output
- json
- sft
- trl
language:
- en
datasets:
- Maelstrome/lora-wave-session-dataset
pipeline_tag: text-generation
---

# lora-wave-session

A unified LoRA adapter on top of **Gemma 4 E2B Instruct** that handles three structured-output surfaces for the WAVE wellness/companion app:

- **`check_in`** — multi-turn patient check-in with structured turn sequencing
- **`phase_narration`** — six-line patient-facing phase narration
- **`reflection`** — reflection plan with a concrete next step

All three surfaces emit strict JSON, no markdown, no analysis voice, in patient-facing tone.

## Repository layout

This repo is the single home for the r16 fine-tune. Everything lives here:

| Path | What | When to use |
|---|---|---|
| `adapter_model.safetensors` + `adapter_config.json` (root) | LoRA adapter (~100 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/`](./tree/main/gguf) | Q4_K_M GGUF (~3.27 GB, single file) + Ollama Modelfile | llama.cpp / Ollama / LM Studio |

> The previously-published `Maelstrome/lora-wave-session-gguf` sibling has been **consolidated into this repo and deleted**. The rank-32 variant has the same layout at [`Maelstrome/lora-wave-session-r32`](https://huggingface.co/Maelstrome/lora-wave-session-r32). Any external link to the old sibling URL will 404.
>
> **Note on browser use:** the GGUF here is a **single 3.27 GB file**, not pre-split. It works directly with llama.cpp / Ollama / LM Studio but **will not load in [wllama](https://github.com/ngxson/wllama)** because it exceeds the 2 GB-per-file `ArrayBuffer` limit. To run this r16 build in-browser, either split it first with `llama-gguf-split --split-max-size 512M` or use the [r32 sibling](https://huggingface.co/Maelstrome/lora-wave-session-r32), which ships pre-split.

## Sibling runs

This is the **rank-16 / 3-epoch RTX 5080** training of the WAVE corpus. The rank-32 / 1-epoch A100 sibling lives at [`Maelstrome/lora-wave-session-r32`](https://huggingface.co/Maelstrome/lora-wave-session-r32) (same subdir layout: adapter at root, `gguf/` subdir; plus `mediapipe/` and `report/`). On the same frozen 428-row test split, r32 wins on every probability metric:

| | **rank-16 (this run)** | rank-32 (sibling) |
|---|---|---|
| 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 [`Maelstrome/lora-wave-session-r32/report/`](https://huggingface.co/Maelstrome/lora-wave-session-r32/tree/main/report) (the comparison + run-report markdown documents).

## 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](https://ai.google.dev/gemma/terms).

## Try it

🌊 **Interactive demo:** [`Maelstrome/lora-wave-session-demo`](https://huggingface.co/spaces/Maelstrome/lora-wave-session-demo) — Gradio Space with surface-specific example prompts.

## Quickstart

### PEFT + Unsloth (CUDA, server-side)

```python
from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="Maelstrome/lora-wave-session",  # PEFT auto-loads base
    max_seq_length=3072,
    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")
```

### Ollama (via the GGUF in `gguf/`)

```bash
ollama create wave-r16 -f - <<EOF
FROM hf://Maelstrome/lora-wave-session/gguf/gemma-4-e2b-it.Q4_K_M.gguf
EOF
ollama run wave-r16
```

### llama.cpp directly

```bash
llama-cli -hf Maelstrome/lora-wave-session:gguf/gemma-4-e2b-it.Q4_K_M.gguf --jinja
```

## 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)

User prompt:

```
<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 (set `max_new_tokens ≥ 224`):

```json
{"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 | **16 / 32 / 0** |
| Target modules | q/k/v/o + gate/up/down (language layers only) |
| Vision/audio layers | Frozen |
| Optimizer | `adamw_8bit` |
| LR | 2e-4, linear schedule |
| Warmup | 64 steps (~5%) |
| Weight decay | 0.001 |
| Max grad norm | 0.3 |
| Batch / grad-accum | 1 / 8 (effective 8) |
| Max sequence length | 3072 |
| Epochs | 3 (1,284 steps) |
| Chat template | `gemma-4` (non-thinking, leading `<bos>` stripped) |
| Response masking | `train_on_responses_only` (Gemma 4 markers) |
| Hardware | Single RTX 5080 (16 GB) |
| Backend | Unsloth 2026.5.2 + Torch 2.10.0 + CUDA 12.8 |

Loss curve: 1.55 (step 1) → 0.76 (avg first 50) → 0.148 (steps 400-500) → 0.112 (last 100). Min 0.0146 at step 1,203. Smooth monotonic decrease, no divergence.

## Evaluation

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

| Metric | Base Gemma 4 E2B | This adapter | Delta |
|---|---|---|---|
| Completion NLL | 4.9327 | **4.7149** | **−0.218** |
| Completion perplexity | 138.76 | **111.59** | **−27.16** |
| Paired wins (LoRA assigned higher prob to reference) | — | **386 / 428 (90.2%)** | — |
| Mean per-example NLL Δ | — | **0.327** nats | 95% bootstrap CI [0.301, 0.352] |
| Median per-example NLL Δ | — | 0.285 nats | — |
| Sign-test p-value | — | **9.54 × 10⁻⁷¹** | overwhelming |

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

### Generation eval (n=8 sanity sample from held-out test)

| Gate | Pass rate |
|---|---|
| JSON validity | 100% (8/8) |
| Schema pass | 100% (8/8) |
| Safety pass | 100% |
| Medical-directive pass | 100% |
| Style / no-markdown / no-analysis-voice | 100% |
| Phase 6-line pass | 100% |
| Reflection next-step pass | 100% |
| Check-in turn sequence pass | 100% |
| Mean tokens/sec (Python QLoRA path) | 10.1 |

This was a small sanity-check sample. For a larger 60-example generation gate sweep on the rank-32 sibling, see [`Maelstrome/lora-wave-session-r32`](https://huggingface.co/Maelstrome/lora-wave-session-r32#evaluation).

## Known quirks

- **Phase narration needs a generation budget of `max_new_tokens ≥ 224`** (256 recommended). The six-line JSON output runs to ~207 tokens; with a lower cap the closing `]}` gets truncated and `JSON.parse` fails. `check_in` is fine at 96; `reflection` at 192.

## Dataset

[`Maelstrome/lora-wave-session-dataset`](https://huggingface.co/datasets/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 a per-surface generation budget ≥ 224 tokens or it will be truncated.

## License

Gemma Terms of Use. See [https://ai.google.dev/gemma/terms](https://ai.google.dev/gemma/terms).

### Framework versions

- PEFT 0.19.1
- Unsloth 2026.5.2
- Transformers 5.5.0
- Torch 2.10.0+cu128