gemma-4-e2b-yemeni-arabic-assistant-GGUF provides GGUF builds of a Gemma 4 E2B Yemeni Arabic assistant model for local inference with llama.cpp-compatible runtimes.
The model is adapted for broad Yemeni Arabic responses. It is intended to answer, rewrite, and translate text using Yemeni-style Arabic rather than defaulting to formal Modern Standard Arabic.
Yemeni Arabic is underrepresented in public LLM resources. This release is intended to support practical local inference and research experimentation around low-resource Yemeni Arabic dialect adaptation.
Model Details
Model repo:yemeni-ai-lab/gemma-4-e2b-yemeni-arabic-assistant-GGUF
Task type: Text generation / assistant responses / dialect rewriting
Intended Use
This model is intended for:
Yemeni Arabic assistant responses
Yemeni dialect rewriting
Translation or adaptation into Yemeni Arabic
Short localized Arabic replies
Yemeni vocabulary and expression experiments
Local inference on consumer hardware
Research on low-resource Arabic dialect adaptation
Example use cases:
text
1User: ترجم للهجة اليمنية: I am tired today and I do not want to go out.
2Assistant: أنا تعبان اليوم وما اشتي أخرج.
text
1User: اكتبها بطريقة يمنية أبسط: لا أريد أن أتكلم عن الموضوع الآن.
2Assistant: ما اشتي أتكلم عن الموضوع ذلحين.
Recommended Prompt
Use a short system prompt that controls assistant behavior:
text
1أنت مساعد يمني ذكي ومختصر.
2جاوب باللهجة اليمنية الطبيعية.
3لا تضف تحية إلا إذا المستخدم بدأ بتحية.
4لا تختم بسؤال أو عرض مساعدة.
5لا تضف رأيك إلا إذا المستخدم طلب رأيك.
6في الترجمة أو إعادة الصياغة: أعطِ النص فقط بدون شرح.
1llama-cli \2 -m gemma-4-e2b-yemeni-arabic-assistant.Q4_K.gguf \3 -p "ترجم للهجة اليمنية فقط. لا تضف مقدمة ولا شرح.\n\nالنص: I am tired today and I do not want to go out."
Different llama.cpp builds may use slightly different flags. If your runtime supports Gemma chat templates, use the model's chat template rather than a hand-written prompt format.
Usage with llama-cpp-python
python
1from llama_cpp import Llama
23llm = Llama(4 model_path="gemma-4-e2b-yemeni-arabic-assistant.Q4_K.gguf",5 n_ctx=1024,6 n_gpu_layers=-1,7)89prompt ="""أنت مساعد يمني ذكي ومختصر.
10جاوب باللهجة اليمنية الطبيعية.
11لا تضف تحية إلا إذا المستخدم بدأ بتحية.
12لا تختم بسؤال أو عرض مساعدة.
1314المستخدم: ترجم للهجة اليمنية: I am tired today and I do not want to go out.
15المساعد:"""1617out = llm(18 prompt,19 max_tokens=160,20 temperature=0.3,21 top_p=0.9,22 repeat_penalty=1.1,23)2425print(out["choices"][0]["text"].strip())
Training Data Summary
The source adapter was trained on 19,609 cleaned Yemeni Arabic assistant-style samples.
The dataset was built from a mixture of locally collected and generated resources, including:
Yemeni social-media style text collected from public/community sources
Yemeni examples derived from Arabic dialect identification data
Yemeni vocabulary and expression references
Local dictionary-style resources
Grounded teacher-model rewrites and expansions
Filtered task examples for translation, rewriting, Q&A, reasoning, and calculations
The final training file was cleaned with rule-based and LLM-assisted review passes. The cleaning focused on reducing:
Non-target dialect leakage such as Gulf/Egyptian expressions
Overused assistant phrases and service endings
Excessive greetings and formal closings
Repetitive openings such as overuse of "والله" and "شوف"
Long, generic explanations where a direct answer was better
Obvious hallucinated or malformed Yemeni expressions
Known unwanted terms from the current data cleaning pass
The final cleaned dataset used for the source fine-tune contained:
19,609 rows
Average assistant response length around 150 characters
Maximum assistant response length capped at 360 characters
Notes on Yemeni Arabic
Yemeni Arabic is not one dialect. It includes substantial regional variation across Sana'a, Taiz, Aden, Ibb, Hadramout, Tihama, Dhamar, Lahj, Al-Bayda, Marib, and other areas. This model targets a broad, practical Yemeni style for assistant use rather than a single city-specific dialect.
The dataset intentionally avoids some expressions that appeared in generated data but were judged unsuitable for the target style, including examples such as non-target vocatives, Gulf-like fillers, and malformed Yemeni phrases.
Limitations
This is an experimental dialect adaptation model.
Known limitations:
Quantized GGUF files may behave differently from the original adapter, especially at lower bitrates.
Very small quantizations can reduce dialect naturalness, reasoning quality, and factual consistency.
The model may still produce Modern Standard Arabic on technical, formal, or unfamiliar topics.
The base instruction model may still show polite assistant behavior unless the prompt and output length are controlled.
The model may occasionally overuse common openings such as "والله" or "شوف".
It may hallucinate factual or cultural information if used without retrieval or source grounding.
The training data includes synthetic and teacher-assisted samples, so not every phrase should be treated as corpus-attested Yemeni speech.
It is not a legal, medical, financial, or religious authority.
It does not represent every Yemeni region equally.
Ethical and Privacy Considerations
The training work used collected and generated Arabic text for dialect adaptation. Public or community-sourced social-media text can contain names, opinions, jokes, and local references. Cleaning passes were applied to reduce low-quality generations and unwanted assistant behavior, but users should not assume the data pipeline guarantees perfect anonymization or factual accuracy.
This model should not be used to impersonate private individuals, generate harassment, or make high-stakes decisions about people.
