ClickBook-Gemma-4-E2B-eu-ar-ru-IQ4_XS

ClickBook Gemma 4 E2B — European + Arabic, IQ4_XS

The smallest ClickBook on-device reading model, and the most thoroughly tested. Vocabulary pruned to Latin, Cyrillic and Arabic: eleven European languages plus Arabic.

1.666 GB.

A reader taps a word in a book; the model explains it in the sense that sentence gives it, writes fresh examples, translates the word, and translates the passage.

Which of the three to use

Take this one if you serve only these eleven languages. It is 178 MB smaller than multi at the same quality, and these are the languages with the most benchmark evidence behind them.

Take multi if you need any Asian or Indic language. The 178 MB it costs buys seven more languages at no measured quality cost — and note that on Android both files exceed Play's 1.5 GB asset-pack limit anyway, so the smaller file does not simplify packaging.

Languages

"Answering" means the model can produce the TRANSLATION and CONTEXT tabs in that language. Those templates exist for English, German and Arabic only; the other eight can be read from, with answers in one of those three.

Turkish is the weakest language in the set by a clear margin — agglutinative morphology fragments hardest under a pruned vocabulary.

Evaluation

90 held-out tapped words in English, German and Arabic, graded 0–100 by an LLM judge against a rubric containing a reference sense.

This is the only build measured at two seeds, giving 78.4 ± 0.7 — worth knowing, because single-seed differences of a point or so between these builds are inside that noise. The apparent 0.2 gap to multi is not meaningful; the 15.6 gap to the unpruned build is.

Quantisation costs 1.3 points against f16 for a 4.7× smaller file. Pruning further — to this build's 180,850 tokens — costs nothing measurable.

Usage

llama-server -m ClickBook-Gemma-4-E2B-eu-ar-ru-IQ4_XS.gguf -c 2048 -ngl 99 --jinja

{
  "messages": [{ "role": "user", "content": "<prompt from prompts.json>" }],
  "temperature": 0.1, "top_k": 40, "top_p": 0.9, "repeat_penalty": 1.05,
  "max_tokens": 111,
  "chat_template_kwargs": { "enable_thinking": false }   // REQUIRED
}

enable_thinking: false is required

Without it the model reasons before answering, spends the whole token budget in reasoning_content, and returns empty content with finish_reason: "length". That is indistinguishable from a broken model. Raise all caps to 1000 first if you want reasoning deliberately.

prompts.json here is filtered to this build's eleven languages. The wider file shipped with multi includes Chinese, Japanese, Korean, Hindi, Tamil, Thai and Hebrew — sending those prompts to this model would hand it text it has no tokens for.

Per-tab caps: MEANING 111, EXAMPLE 222, TRANSLATION 111, CONTEXT 444. Measured on the benchmark, 0 of 360 panels reached them.

Limitations

• Not a chat model. Tuned for four narrow tasks driven by the supplied prompts.
• Failures concentrate on polysemous common words — bank, charge, Schloss — 15 of 90 items.
• No on-device dictionary is bundled. An external sense resource measured +12.7 points on this benchmark but is not included.
• Turkish is materially weaker than the rest.

License and provenance

Apache License 2.0, matching the base model, google/gemma-4-E2B-it. Google also publishes a Gemma 4 license page, linked from the upstream card.

Modifications, as Apache 2.0 requires derivative works to state:

1. Vocabulary pruned from 262,144 to 180,850 tokens, retaining Latin, Cyrillic and Arabic and removing CJK, Hangul, Devanagari, Thai, Hebrew, Greek and other scripts the product does not serve.
2. Quantised to IQ4_XS with Q2_K token embeddings under an importance matrix.

No weights were fine-tuned, distilled or retrained.

Gemma is a trademark of Google LLC. This is an independent derivative, not endorsed by or affiliated with Google.