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kessenma/gemma4-e4b-german-tutor-v4-4bit
gemma4-e4b-german-tutor-v4-4bit is a image-text-to-text model from kessenma. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as gemma.
QLoRA fine-tune of google/gemma-4-e4b-it for German grammar tutoring, quantized to 4-bit (group size 64, affine) for on-device inference with MLX. Built for an iOS flashcard app; the model corrects learner sentences a…
Downloads · 30 days
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21% of all-time downloads
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From the Hugging Face model README
QLoRA fine-tune of google/gemma-4-e4b-it for German grammar tutoring, quantized to 4-bit
(group size 64, affine) for on-device inference with MLX. Built for an iOS flashcard app; the
model corrects learner sentences and chats in natural, colloquial German.
This build supersedes kessenma/gemma4-e4b-german-tutor-4bit (v1).
All scores use the app-guard convention: a reply only counts if the app's parser would actually show it to the learner. Suites are frozen JSON eval sets scored offline.
| suite | items | v4 | v1 (superseded) |
|---|---|---|---|
| core grammar (v0) | 60 | 51 (85%) | 54 (90%) |
| extension (v1ext) | 61 | 56 (92%) | 57 (93%) |
| holdout (v2) | 82 | 75 (91%) | 70 (85%) |
| combined | 203 | 182 (90%) | 181 (89%) |
Paired per-item comparison vs v1: 170/203 agree, 11 items only v1 gets, 12 only v4 gets (exact McNemar p = 1.0). Grammar ability is equivalent; the differences are behavioral:
In short: v4 almost never marks correct German wrong, and it talks like a person instead of a worksheet. The trade is that it lets a few more real errors slide than v1 did.
42,841 instruction rows (corpus v4): teacher-generated grammar corrections balanced to 70% fix / 30% ok per phenomenon across 15 grammar phenomena, plus a conversational slice for register. Teachers: gemma-4-31B (bulk), Claude Sonnet (judgment-heavy phenomena), with every row passing LanguageTool + spaCy validation and phenomenon-shape gates.
Given a learner sentence, the model answers either OK (sentence is correct) or:
FIX: <corrected sentence>
WHY: <one-line explanation>
HINT: <question that nudges the learner>
Requires a recent mlx-vlm / mlx-swift-lm (Gemma 4 support). Single-shard safetensors;
the lora/ adapter is not included here (archived separately with the fp16 merge).