proofreader-0.6B-MLX-6bit

A small style proofreader for notes, built to run while you type. Given a sentence or a paragraph, it points at the wordy or clumsy phrase, copied verbatim from the text, and proposes a shorter one; when the text is fine it says nothing. It answers with one JSON object and stops.

Text (English):
Due to the fact that the office is closed, we will send the documents on Monday.

{"suggestions": [{"type": "style", "original": "Due to the fact that", "replacement": "Because"}]}

It is the style checker of the wordspace notes app, where spelling and grammar come from the operating system and the model handles style only. It works in Italian, English, Spanish, French, German and Portuguese.

Fine-tuned from Qwen3-0.6B-Base with LoRA, fused, and quantized to 6 bits for MLX. See NOTICE for the full list of changes.

Use

pip install mlx-lm
hf download wordspace/proofreader-0.6B-MLX-6bit --local-dir proofreader
cd proofreader && python example.py

Build the two chat messages with protocol.py (the system prompt must stay identical, character for character), apply the tokenizer's chat template, and generate greedily. Read the first JSON object of the answer and keep the style suggestions whose original appears in the text (protocol.style_suggestions). The model stops at its end-of-text token, after 18 tokens on average.

Results

Blind test. 217 sentences (103 wordy, 114 clean) written for this test by six independent authors, one per language, who saw neither the training templates nor the earlier tests; a second reader relabelled everything and cases they disagreed on were dropped. None shares eight consecutive words with the training data. Compared on the same sentences, with the same prompt, with gemma-3-4b-it (QAT, 4 bit), the model the app used for this task, and with the previous version of this model.

	this model	previous version	gemma-3-4b
wordy phrases found	82/103	65/103	26/103
correction judged good	50/103	18/103	4/103
· phrase in no training template (58)	18/58	2/58	1/58
clean sentences touched (the harm)	8/114	5/114	18/114

"Judged good": the corrected sentence, read without knowing which model wrote it, is grammatical, keeps the meaning and is tighter. Two separate LLM judges, good only if both agree (they agreed on 172 of 173). Against the previous version: found p = 0.002, judged good p < 0.001, harm p = 0.51 (exact McNemar on the same sentences); against gemma all p < 0.001 except harm, p = 0.052. Wordy phrases found, by language: German 13/16, English 15/16, Spanish 15/17, French 14/19, Italian 14/18, Portuguese 11/17.

Hand-written probe. 192 sentences, 32 per language, by the authors of the training templates: 60 with a phrase the templates know, in new sentences; 36 with phrases the original templates did not contain; 96 clean.

	this model	gemma-3-4b	p
wordy phrases found	88/96	27/96	< 0.001
· known phrases, new sentences	59/60	18/60	< 0.001
· other phrases	29/36	9/36	< 0.001
clean sentences touched (the harm)	2/96	8/96	0.070
seconds per sentence (median, Apple silicon)	0.15	0.64	—

On the eight wordy and eight clean sentences of the app's own test: 7/8 found against 3/8, no clean sentence touched by either. The 6-bit model gives the same answers as the full-precision one within noise (blind test: 82 vs 82 found, 8 vs 8 touched; probe: 88 vs 88 found, 2 vs 3 touched).

Limits

• Style only: wordy phrases, nominal style ("carry out an inspection of"), redundancies ("repeat again"). It is not trained to fix spelling or grammar and should leave them alone.
• On phrases outside what it has learned, it proposes a good correction for fewer than one sentence in three (18/58 in the blind test). Treat suggestions as suggestions.
• On held-out validation notes it touches 9% of the clean ones (15/173): more in clean twins of wordy sentences (16%) and above all in German (7 of 29 clean German cases), against 0 of 35 in English. German is the weakest language.
• Trained on short notes: sentences and paragraphs of a few sentences.

Training

24,000 synthetic cases generated from hand-written templates in six languages, about 185 wordy and 130 clean templates per language, most of them written by authors who had not seen the others (so that the model learns the task, not one writer's habits): wordy sentences with their defects marked, the same sentences already concise (minimal pairs, so the model learns the phrasing and not the topic), clean notes, and paragraphs mixing them; almost half have nothing to correct. One template in five was kept for validation only. LoRA rank 16 on all layers, AdamW, learning rate 1e-4, gradient clipping at norm 1, loss on the answer tokens only, every answer closed by the end-of-text token so that the model stops. The checkpoint was chosen by the task on validation (wordy phrases found and clean sentences left alone), not by the loss. Any generated case sharing eight consecutive words with a test sentence was discarded. No personal notes were used.