MathematicianNLPer/GemMaroc-27b-it

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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

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

_{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)

1. 1.Data Translate a 44 K reasoning slice of TULU 50K into Darija, keeping 20 % English for cross‑lingual robustness.
2. 2.LoRA SFT Rank 16, α = 32, 3 epochs, bf16, context 2 048.
3. 3.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{skiredj2025gemmarocunlockingdarijaproficiency,
      title={GemMaroc: Unlocking Darija Proficiency in LLMs with Minimal Data}, 
      author={Abderrahman Skiredj and Ferdaous Azhari and Houdaifa Atou and Nouamane Tazi and Ismail Berrada},
      year={2025},
      eprint={2505.17082},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.17082}, 
}