--- license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license base_model: google/gemma-4-E2B-it library_name: gguf pipeline_tag: text-generation tags: - gguf - llama.cpp - on-device - mobile - vocabulary-pruned - quantized language: - en - de - ar - fr - es - it - pt - nl - pl - tr - ru --- # 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 | model | size | languages | score | |---|---:|---|---:| | **this one** | **1.666 GB** | 11 — Latin, Cyrillic, Arabic | **78.4** | | [multi](https://huggingface.co/Clickbook/ClickBook-Gemma-4-E2B-multi-IQ4_XS) | 1.844 GB | 18 — adds CJK, Korean, Hindi, Tamil, Thai, Hebrew | 79.2 | | [allscripts](https://huggingface.co/Clickbook/ClickBook-Gemma-4-E2B-allscripts-IQ4_XS) | 1.950 GB | every script | 63.6 | **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 | language | reading | answering | evidence | |---|:--:|:--:|---| | English, German, Arabic | yes | yes | 90-item benchmark, **two seeds** | | French | yes | not yet | 183 items, older build: 86.3 | | Portuguese | yes | not yet | 183 items: 85.7 | | Spanish, Italian | yes | not yet | 183 items: 81.3 | | Russian | yes | not yet | 183 items: 81.2 | | Dutch | yes | not yet | 183 items: 79.2 | | Polish | yes | not yet | 183 items: 76.7 | | Turkish | yes | not yet | 183 items: **69.3 — weakest** | "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. | build | vocabulary | size | score | |---|---:|---:|---:| | same weights at f16 | 231,955 | 8.676 GB | 80.5 | | multi (18 languages) | 231,955 | 1.844 GB | 79.2 | | **this model** | 180,850 | **1.666 GB** | **79.0** / 77.7 (two seeds) | | unpruned vocabulary | 262,144 | 1.950 GB | 63.6 | **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 ```bash llama-server -m ClickBook-Gemma-4-E2B-eu-ar-ru-IQ4_XS.gguf -c 2048 -ngl 99 --jinja ``` ```jsonc { "messages": [{ "role": "user", "content": "" }], "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`](https://huggingface.co/google/gemma-4-E2B-it). Google also publishes a [Gemma 4 license page](https://ai.google.dev/gemma/docs/gemma_4_license), 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.