mindtype-tagger-tiny-v1.6
MindType's own grammar and spelling correction model, trained from scratch by Mindscend for an on-device keyboard (iOS and Android). Text never leaves the phone.
- Tiny version for older phones: 12.7M parameters (half of mindtype-tagger-v1.6), encoder-only transformer (6 layers, d=256), own 16k byte-level BPE tokenizer.
- Predicts one edit label per word (KEEP / DELETE / REPLACE_x / APPEND_x / case) in a single pass, so it is fast, tiny, and cannot invent words or rewrite correct text.
- Core ML package (8-bit weights) for iOS 18+, and ONNX (int8) for Android and servers.
What changed: the v1.6 recipe (past-tense training pairs) on the tiny encoder, trained for 22,600 steps. Tense errors went from almost never fixed (F0.5 0.11) to mostly fixed (F0.5 0.85, 61.3% exact), at nearly the same accuracy as the full-size v1.6 for half the size.
Results (MindType evaluation suite, 4,356 held-out cases)
Settings: KEEP bias 2.0 and the plausibility guard used in the MindType apps.
| Set | n | F0.5 | Exact | False-positive rate | Lost protected spans |
|---|---|---|---|---|---|
| casual_clean | 118 | – | 0.983 | 0.017 | 0.000 |
| casual_noisy | 109 | 0.909 | 0.752 | 0.000 | 0.000 |
| coedit | 579 | 0.470 | 0.107 | 0.000 | 0.000 |
| formal_clean | 1000 | – | 0.956 | 0.044 | 0.000 |
| formal_noisy | 1000 | 0.861 | 0.627 | 0.000 | 0.000 |
| golden | 50 | 0.914 | 0.820 | 0.077 | 0.000 |
| preserve | 1000 | 0.830 | 0.635 | 0.000 | 0.000 |
| preserve_clean | 500 | – | 1.000 | 0.000 | 0.000 |
| overall | 4356 | 0.788 | 0.693 | 0.029 | 0.000 |
False-positive rate on 2,000 held-out casual messages (never used for training): 1.60% (full-size v1.6: 1.65% on the same messages).
On the *_clean sets there is nothing to fix; the false-positive rate is what counts there.
Compared with fine-tuned small LLMs and earlier versions (same suite)
| Model | Size | F0.5 ↑ | Exact ↑ | False positives ↓ | Lost names/numbers ↓ |
|---|---|---|---|---|---|
| Gemma 3 270M instruct, zero-shot | 270M | 0.122 | 0.2% | 99.6% | 3.9% |
| Gemma 3 270M + LoRA r32 | 270M | 0.571 | 57.4% | 23.1% | 2.9% |
| SmolLM2 360M + LoRA r32 | 360M | 0.663 | 63.1% | 20.1% | 0.3% |
| mindtype-tagger-v1.1 (keep bias 2, guard) | 26M | 0.748 | 68.3% | 8.65% | 0.0% |
| mindtype-tagger-v1.2 (keep bias 2, guard) | 26M | 0.800 | 72.4% | 3.1% | 0.0% |
| mindtype-tagger-v1.5 (keep bias 2, guard) | 26M | 0.799 | 71.2% | 2.4% | 0.0% |
| mindtype-tagger-v1.6 (keep bias 2, guard) | 26M | 0.802 | 71.3% | 2.8% | 0.0% |
| mindtype-tagger-tiny-v1.6 (keep bias 2, guard) | 13M | 0.788 | 69.3% | 2.9% | 0.0% |
Error types (separate test sets, not used for training)
| Error type | n | full-size v1.6 F0.5 | tiny v1.6 F0.5 | tiny v1.6 exact |
|---|---|---|---|---|
| Real misspellings ("recieve") | 500 | 0.957 | 0.958 | 91.6% |
| Dropped words ("I going home") | 500 | 0.683 | 0.638 | 32.2% |
| Present tense about the past ("Yesterday I go") | 248 | 0.885 | 0.850 | 61.3% |
Dropped words are now the weakest; they're next.
Files
| File | What it is |
|---|---|
model.safetensors |
PyTorch weights (fp32): encoder + per-token classification head |
config.json |
architecture (layers, width, heads, vocabulary and label counts) |
tokenizer.json |
16k byte-level BPE tokenizer (Hugging Face tokenizers format) |
labels.json |
the 15,000 edit labels, index → label |
plausible_pairs.json |
the word pairs the plausibility guard accepts |
coreml/MindTypeTagger.mlpackage |
Core ML model (8-bit weights, 13 MB), inputs padded to 16/32/64/96/160 tokens; 99.92% label agreement with fp32 |
onnx/mindtype_tagger.onnx |
ONNX model (opset 17, int8 dynamic quantization, 13 MB), inputs ids, mask (int64, [1, T]), output logits; 98.8% label agreement with fp32 |
metrics.json, log.jsonl, run.json |
evaluation results and training logs |
Training
- Pretraining: masked language modeling on FineWeb-Edu (ODC-BY).
- Correction: 12.02M (noisy, clean) pairs (22,600 steps at batch 256): synthetic phone-typing errors, CoEdIT GEC (Apache-2.0), and casual messages written by a local open-weight model (Qwen2.5-14B-Instruct) and filtered for correctness, plus real misspellings, dropped words and tense errors (1.2% of pairs: past-tense sentences with a tense error). 30% are already-correct text, so the model learns to leave good text alone.
Hardware: one RTX 3080 Ti laptop GPU; the correction step took about 35 minutes.
How it works
Text is split into words and punctuation; each word gets a label from labels.json: $KEEP, $DELETE,
$REPLACE_<word>, $APPEND_<word> (insert after), or a case change. The corrected sentence is the labels applied
to the words, and the model is run again (up to 3 passes) on its own output. One sentence takes a few milliseconds on a
phone CPU (Core ML) and on a laptop CPU (ONNX Runtime).
Inference settings used for the results: KEEP bias 2.0 (added to the $KEEP logit; higher = fewer, surer fixes).
Names, numbers, emails and URLs are never changed.
Limitations
English only, sentence-level, experimental. Fixes what's there; it doesn't reword or change tone. About 40% of tense errors and two thirds of dropped words are still missed. It can still change correct text (2.9% of correct sentences in our suite, 1.6% of real casual messages).
License
Apache-2.0 (weights, tokenizer, Core ML and ONNX models). Training data: FineWeb-Edu (ODC-BY), CoEdIT (Apache-2.0).