Model Card for Model ID
GemMaroc‑27B
Unlocking Moroccan Darija proficiency in a state‑of‑the‑art large language model, trained with a minimal‑data, green‑AI recipe that preserves Gemma‑27B’s strong reasoning abilities while adding fluent Darija generation.
Model at a glance
| Details | |
|---|---|
| Model ID | AbderrahmanSkiredj1/GemMaroc-27b-it |
| Base model | google/gemma-3-27b |
| Architecture | Decoder‑only Transformer (Gemma 3) |
| Parameters | 27 billion |
| Context length | 2 048 tokens |
| Training regime | Supervised fine‑tuning (LoRA → merged) on 50 K high‑quality Darija/English instructions TULU‑50K slice |
| Compute budget | 48 GPU·h (8 × H100‑80GB × 6 h) – ≈ 26 kWh / 10 kg CO₂e |
| License | Apache 2.0 |
Why another Darija model?
- Inclusive AI > 36 million speakers of Moroccan Arabic remain underserved by open LLMs.
- Quality‑over‑quantity A carefully curated 50 K instruction set surfaces Darija competence without sacrificing cross‑lingual reasoning.
- Green AI GemMaroc achieves Atlas‑Chat‑level Darija scores using < 2 % of the energy.
Benchmark summary
| Model | Darija MMLU | Darija HellaSwag | GSM8K @5 | HellaSwag (EN) |
|---|---|---|---|---|
| Atlas‑Chat‑27B | 61.9 % | 48.4 % | 82.0 % | 77.8 % |
| GemMaroc‑27B | 61.6 % | 60.5 % | 84.2 % | 79.3 % |
Zero‑shot accuracy; full table in the paper.
Quick start
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "AbderrahmanSkiredj1/GemMaroc-27b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
temperature=0.7,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
)
messages = [
{"role": "user", "content": "شنو هي نظرية ‘butterfly effect’؟ فسّرها بدارجة ونقّط مثال بسيط."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(pipe(prompt)[0]["generated_text"][len(prompt):])
Chat template (Gemma 3 format)
The tokenizer provides a baked‑in Jinja template that starts with a begin‑of‑sequence token (<bos>), then alternates user/model turns, each wrapped by <start_of_turn> … <end_of_turn> markers. When you set add_generation_prompt=True it ends after the opening model tag so the model can continue:
<bos><start_of_turn>user
{user message}<end_of_turn>
<start_of_turn>model
The assistant will keep generating tokens until it decides to emit <end_of_turn>.
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
No manual token juggling required—the call above handles BOS, turn delimiters, and newline placement automatically.
Pre‑quantised checkpoints will be published under the same repo tags (gemmaroc‑27b‑awq‑int4, gemmaroc‑27b‑gguf‑q4_k_m).
Training recipe (one‑paragraph recap)
- Data Translate a 44 K reasoning slice of TULU 50K into Darija, keeping 20 % English for cross‑lingual robustness.
- LoRA SFT Rank 16, α = 32, 3 epochs, bf16, context 2 048.
- Merge & push Merge LoRA into base weights (
peft.merge_and_unload), convert to safetensors, upload.
Limitations & ethical considerations
- Sentiment and abstractive summarisation still trail state‑of‑the‑art.
- Tokeniser is unchanged; rare Darija spellings may fragment.
- Model may inherit societal biases present in pre‑training data.
- No RLHF / RLAIF safety alignment yet – apply a moderation layer in production.
Citation
If you use GemMaroc in your work, please cite:
@misc{abderrahman2025gemmaroc,
author = {Skiredj, Abderrahman and Azhari, Ferdaous and Atou, Houdaifa and Tazi, Nouamane and Berrada, Ismail},
title = {{GemMaroc: Unlocking Darija Proficiency in LLMs with Minimal Data}},
year = {2025},
publisher = {Zenodo},
doi = {10.5281/zenodo.15492072},
url = {https://doi.org/10.5281/zenodo.15492072}
}
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