AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF

This model was converted to GGUF format from GemMaroc/GemMaroc-4b-tulu using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF --hf-file gemmaroc-4b-tulu-q4_k_m-imat.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF --hf-file gemmaroc-4b-tulu-q4_k_m-imat.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF --hf-file gemmaroc-4b-tulu-q4_k_m-imat.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo AbderrahmanSkiredj1/GemMaroc-4b-tulu-Q4_K_M-GGUF --hf-file gemmaroc-4b-tulu-q4_k_m-imat.gguf -c 2048

---

library_name: transformers tags: - MoroccanArabic - Darija - GemMaroc datasets: - GemMaroc/TULU-3-50k-darija-english language: - ar - ary - en base_model: - google/gemma-3-27b-it

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)

1. Data Translate a 44 K reasoning slice of TULU 50K into Darija, keeping 20 % English for cross‑lingual robustness.
2. LoRA SFT Rank 16, α = 32, 3 epochs, bf16, context 2 048.
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}, 
}