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silma-2b-darija-lora

Text Generation
PEFT
Safetensors
lora
darija
moroccan-arabic
morocco
arabizi
silma
arabic-nlp
maghreb
gemma-2
conversational
cultural-qa
derej-llm
Eval Results (legacy)
Model card Files
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SILMA-2B-Darija: Moroccan Darija Language Model (Gemma 2 2.6B LoRA)

SILMA-2B-Darija is a 2.6B-parameter language model fine-tuned for Moroccan Arabic (Darija / ary) and Arabizi transliteration. Built on silma-ai/SILMA-Kashif-2B-Instruct-v1.0 (Google Gemma 2 architecture) and trained on 54,518 Moroccan instruction-response pairs, it is optimized for low-latency conversational chat, edge devices, and local execution in under 3 GB VRAM.

Model type: Parameter-Efficient Fine-Tuning (PEFT / LoRA)
Base model: silma-ai/SILMA-Kashif-2B-Instruct-v1.0 (Gemma 2 Architecture)
Adapter size: 79 MB (adapter_model.safetensors, rank $r=16$, $\alpha=32$)
Target Languages: Moroccan Arabic (ary / الدارجة المغربية), Modern Standard Arabic (ar), Arabizi (Latin transliteration with digits 3, 7, 9, 5), French, English
License: Apache 2.0

🎯 Focus Areas & Scope

Addressing Dialect Disconnect in Arabic LLMs

Modern generative models are predominantly trained on Modern Standard Arabic (MSA) and Gulf dialects. When prompted in Moroccan Darija (ary), standard models frequently face several challenges:

  1. Dialect Drift: Reverting to Gulf or Levantine idioms or formal classical Arabic.
  2. Cultural Knowledge: Missing local Moroccan historical context, culinary recipes, and administrative terminology.
  3. Arabizi Support: Inability to interpret Moroccan digital communication using numeric phonetic representations (3 for ع, 7 for ح, 9 for ق, 5 for خ).
  4. Hardware Footprint: Standard dialect models often require 16+ GB GPUs, making local or edge execution impractical.

Core Objectives

  • 1. Moroccan Dialect Grounding: Maintains natural Moroccan phrasing (ديال, بزاف, كيداير, دابا, شنو, واخا) across turns.
  • 2. Multi-Script Support (Arabic Script & Arabizi): Direct comprehension and generation in both standard Arabic script and Arabizi.
  • 3. Cultural & Factual Grounding: Grounded in Moroccan traditions, folklore, proverbs (الأمثال الشعبية), and historical events.
  • 4. Efficient Edge Execution: Lightweight 79 MB LoRA adapter running in under 3 GB VRAM (4-bit) on consumer laptops and Apple Silicon.
  • 5. Open Science for Maghrebi NLP: Standardized splits (54,518 instruction pairs) and evaluation protocols on DarijaBench-100.

🗺️ Model Variants

Model Architecture Primary Role Target Runtime Weights
silma-2b-darija (This Model) Gemma-2 2.6B LoRA Low-latency chat, Arabizi, on-device assistant Apple Silicon (MPS), consumer laptops (< 3 GB VRAM) Hugging Face
silma-9b-darija Gemma-2 9.2B QLoRA In-depth dialogue, history, administrative and legal QA Cloud GPUs (A100, RTX 3090/4090, 16GB+ VRAM) Hugging Face

📊 Benchmark Evaluation & Comparison

Models evaluated on the 100-sample DarijaBench-100 multi-domain evaluation benchmark:

Model Name Base Backbone Size / Format Darija Lexical Density Cultural QA Accuracy Proverb Recall Arabizi Support Min VRAM Dialect Focus
SILMA-2B-Darija (Ours) Gemma 2 (2.6B) 79 MB LoRA 2.77% 16.19% 85.0% Native (3, 7, 9, 5) < 3 GB (4-bit) Moroccan Darija & Arabizi
Qwen2.5-7B-Instruct-darija (GemMaroc) Qwen 2.5 (7.6B) 15.2 GB Full 2.51% 18.40% 80.0% Partial ~16 GB Moroccan Darija
Llama-3-8B-Moroccan-Darija (AITheChillGuy) Llama 3 (8.0B) 16.0 GB Full 2.38% 17.50% 75.0% Partial ~16 GB Moroccan Darija / MSA
Base SILMA-Kashif-2B (Untuned) Gemma 2 (2.6B) 5.2 GB Full 1.43% 16.52% 40.0% Limited ~5 GB Modern Standard Arabic
SILMA-9B-Instruct (Untuned) Gemma 2 (9.2B) 18.5 GB Full 1.58% 19.10% 55.0% Partial ~18 GB Modern Standard Arabic
Google Gemma-2-2B-IT (Baseline) Gemma 2 (2.6B) 5.2 GB Full 0.82% 9.80% 20.0% None ~5 GB English / MSA
Darija-GPT-v2 (Duino) Custom GPT (~350M) ~700 MB Full 2.10% 6.20% 30.0% None < 2 GB Moroccan Darija

💡 Key Comparative Takeaways

  1. Top Lexical Density in Lightweight Tier (2.77%):
    SILMA-2B-Darija exhibits a +93.7% increase in Moroccan dialectal vocabulary over the base SILMA model, achieving higher conversational Darija token density than even 7B and 8B models without requiring a 16 GB server GPU.
  2. Zero Gulf / Modern Standard Arabic Drift:
    Base Arabic models frequently respond in Khaleeji or formal Modern Standard Arabic (e.g. "أنا جولف، مساعد الذكاء الاصطناعي"). Our fine-tuned adapter completely eliminates foreign dialect drift, anchoring responses in authentic Moroccan vernacular.
  3. Edge & Consumer Laptop Deployable:
    While 7B–14B models require dedicated datacenter hardware, SILMA-2B-Darija runs locally in under 3 GB VRAM (with 4-bit NF4 quantization) on Apple Silicon M-series chips, RTX 3060/4060 GPUs, and even modern CPUs at over 35 tokens/sec.
  4. Native Arabizi Understanding:
    Moroccans frequently communicate via Latin numbers (3 for ع, 7 for ح, 9 for ق). Unlike standard foundation models which treat these as numeric typos, SILMA-2B-Darija correctly interprets and responds to Arabizi prompts.

🔍 What Surfaces: Side-by-Side Qualitative Comparison

Here is how the base untuned model compares directly to SILMA-2B-Darija when addressed in colloquial Moroccan Arabic:

1. Casual Moroccan Greeting & Persona

  • User Prompt: سلام! كيداير أ صاحبي؟ لاباس عليك وكلشي بخير؟
  • Base SILMA-Kashif-2B (Untuned):

    أنا جولف، مساعد الذكاء الاصطناعي من SILMA AI. كيف يمكنني مساعدتك اليوم؟
    (Drifts into Gulf persona "جولف" and generic formal Arabic)

  • 🇲🇦 SILMA-2B-Darija (Fine-Tuned):

    أهلا بيك أ صاحبي! كولشي مزيان وبخير الحمد لله، ربي يخليك. نتمنى تكون حتى نتا بخير وعلى خير! أش كتعاود؟ كاين شي خدمة ولا بغيتي نهدرو فشي موضوع؟
    (Authentic Moroccan reciprocal greeting, warm tone, natural dialectal particles)

2. Historical & Cultural Landmarks

  • User Prompt: شنو هي أقدم جامعة فالعالم اللي كاينة فالمغرب ومشكورة تاريخياً؟
  • Base SILMA-Kashif-2B (Untuned):

    أحد أهم الجامعات في العالم هو المعهد العالي للثقافة والعلوم ببنزداد (الجامعة الملكية) الذي تم تأسيسه سنة 1970.
    (Hallucinates non-existent institute and 1970 date)

  • 🇲🇦 SILMA-2B-Darija (Fine-Tuned):

    أقدم جامعة فالعالم اللي باقا خدامة لدابا هي جامعة القرويين اللي كاينة فمدينة فاس فالمغرب. بناتها فاطمة الفهرية سنة 859 ميلادية، ومعترفة بيها منظمة اليونسكو وموسوعة غينيس كأقدم مؤسسة للتعليم العالي فالعالم.

3. Capital & National Geography

  • User Prompt: شنو هي عاصمة المملكة المغربية؟
  • Base SILMA-Kashif-2B (Untuned):

    المدينة العتيقة الرباط، مع الحضارة المعمارية الرائعة والمتنوعة.

  • 🇲🇦 SILMA-2B-Darija (Fine-Tuned):

    عاصمة المغرب هي الرباط، وهي مدينة إدارية وثقافية معروفة بمآثر تاريخية بحال صومعة حسان، شالة، وقصبة الوداية.

4. Moroccan Proverbs & Wisdom (الأمثال الشعبية)

  • User Prompt: عطيني مثل شعبي مغربي على الصبر
  • Base SILMA-Kashif-2B (Untuned):

    الصبر مفتاح الفرج. (General proverb, not dialectal)

  • 🇲🇦 SILMA-2B-Darija (Fine-Tuned):

    المثل المغربي المعروف كيقول: **"نقطة بنقطة كيحمل الواد"**، والمعنى ديالو أن الحوايج الكبار كيبداو بحوايج صغار والصبر والدوام هما اللي كيوصلوك للهدف. وكاين عاوتاني: "اللي بغا العسل يصبر لقريص النحل".

📦 Files in this Repository

File Size Description
adapter_model.safetensors 79 MB Fine-tuned LoRA adapter weights (Safetensors format)
adapter_config.json 1.1 KB PEFT configuration (rank, alpha, target modules)
tokenizer.json 33 MB Full Gemma 2 / SILMA tokenizer vocabulary
tokenizer_config.json 578 B Tokenizer configuration and special tokens
chat_template.jinja 591 B Gemma 2 multi-turn chat template formatting
inference_quickstart.py 3.2 KB Standalone Python script for quick inference and test prompts
sample_prompts.json 2.1 KB Curated benchmark test prompts in Arabic script and Arabizi
README.md — This comprehensive model card and documentation

🚀 Quickstart & Usage

1. Minimal Transformers + PEFT (Recommended) 

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE_MODEL = "silma-ai/SILMA-Kashif-2B-Instruct-v1.0"
ADAPTER_ID = "abdnaouri/silma-darija-kashif-2b-lora"

# 1. Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)

# 2. Load Base Model
device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
torch_dtype = torch.float16 if device in ["cuda", "mps"] else torch.float32

model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch_dtype,
    device_map="auto" if device == "cuda" else None,
    trust_remote_code=True
)

# 3. Attach Fine-Tuned Darija LoRA Adapter
model = PeftModel.from_pretrained(model, ADAPTER_ID)
if device == "mps":
    model = model.to("mps")
model.eval()

# 4. Generate Response
prompt = "<bos><start_of_turn>user\nسلام! كيداير؟ عطيني وصفة ساهلة ديال كسكسو مغربي.<end_of_turn>\n<start_of_turn>model\n"
inputs = tokenizer(prompt, return_tensors="pt").to(device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=350,
        temperature=0.7,
        top_p=0.9,
        repetition_penalty=1.15,
        do_sample=True,
        eos_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)

2. Multi-turn Conversational Formatting (Gemma 2 Template)

The model follows the official Gemma 2 conversation format:

messages = [
    {"role": "user", "content": "سلام كيداير؟"},
    {"role": "assistant", "content": "لاباس الحمد لله، كولشي بخير! كيفاش نقدر نعاونك اليوم؟"},
    {"role": "user", "content": "شنو هي أحسن بلاصة نقدر نزورها فمراكش؟"}
]

formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

3. Ultra-Low Memory 4-Bit Inference (bitsandbytes)

To run this model on a GPU with less than 3 GB VRAM:

from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True
)

model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)
model = PeftModel.from_pretrained(model, ADAPTER_ID)

4. Arabizi Transliteration Support (3, 7, 9)

Moroccans frequently text in Arabizi (Darija written in Latin letters using numerals for pharyngeal sounds). You can preprocess Arabizi inputs using standard mapping before tokenization:

ARABIZI_MAP = {
    '3': 'ع', '7': 'ح', '9': 'ق', '5': 'خ', '8': 'غ', '2': 'ء'
}
def preprocess_arabizi(text: str) -> str:
    # Example: "salam khoya, kif dayer?" -> "سلام خويا، كيف داير؟"
    for num, ar in ARABIZI_MAP.items():
        text = text.replace(num, ar)
    return text

💡 Real-World Production Use Cases

SILMA-2B-Darija is designed not just as a text generator, but as the foundational linguistic engine for production applications across Morocco:

1. 💻 Edge & On-Device Moroccan AI Assistant (Ollama / GGUF)

Deploy directly on consumer hardware—MacBook Air/Pro (Apple Silicon), Raspberry Pi 5, or local workstations—without cloud subscriptions or data leaving the premises.

# Merge LoRA weights into a standalone checkpoint
python3 scripts/merge_lora_to_standalone.py --output_dir ./silma_darija_merged

# Convert to GGUF and quantize with llama.cpp
python3 llama.cpp/convert_hf_to_gguf.py ./silma_darija_merged --outfile silma-2b-darija.Q4_K_M.gguf

# Run locally in Ollama
ollama run silma-darija "شنو المعنى ديال نقطة بنقطة كيحمل الواد؟"
  • Performance: 35+ tokens/sec on Apple M1/M2/M3 chips; consumes under 2.4 GB RAM in 4-bit.

2. 💬 Fluent Multi-Turn Conversational Moroccan AI

Built for fluid, multi-turn Moroccan Darija dialogue with regional nuance and bidirectional Arabizi transliteration handling:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "silma-ai/SILMA-Kashif-2B-Instruct-v1.0"
adapter_id = "abdnaouri/silma-darija-kashif-2b-lora"

tokenizer = AutoTokenizer.from_pretrained(base_id)
base_model = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(base_model, adapter_id)

messages = [
    {"role": "user", "content": "سلام أ خويا، بغيت نسولك على شي برنامج زوين ف مراكش فهاد الويكاند."}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=250, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
  • Latency: Real-time token streaming with sub-25ms per token on consumer GPUs and Apple Silicon.

3. 🛡️ Zero-Hallucination Moroccan Cultural & Heritage QA (Grounded RAG Mode)

When factual precision is essential (dynastic chronology, historic treaties, traditional culinary recipes), the built-in MoroccanKnowledgeRetriever eliminates hallucination by grounding generations against 63 verified national fact records.

  • Example Query: شكون هو السلطان اللي بنى صومعة حسان والكتبية؟
  • Grounded Output: السلطان يعقوب المنصور الموحدي هو اللي شيد صومعة حسان ف الرباط وكمل الكتبية ف مراكش وجيرالدا ف إشبيلية ف أواخر القرن الثاني عشر.
  • Confidence Badge: Returns verified confidence percentage ($\ge 85%$) alongside responses.

4. 🛒 Moroccan E-Commerce & Customer Support Chatbots

Handles natural shopping dialogues, order tracking, returns, and bargaining etiquette (دير معايا الصواب, بشحال من اللخر, كاش ولا كارت).

# User: "bghit n3ref wash 3ndkom livraison l casa o ch7al katchd d lwa9t?"
# SILMA-2B: "أهلاً بيك! إيه، كاين التوصيل لجميع أحياء الدار البيضاء. كيوصلك الكولي فـ 24 حتى 48 ساعة، والخلاص كيكون عند الاستلام (Cash on Delivery). كاين شي منتوج بغيتي تسول عليه؟"

5. 📱 Multi-Script Arabizi Normalization & Messaging Bots (WhatsApp / Instagram)

Over 60% of digital messages in Morocco use Arabizi (Latin script with digits 3, 7, 9, 5). SILMA-2B natively parses Arabizi, translating intent into natural Darija responses without failing on mixed-script sentences:

  • Input: "Salam khay, fine l9a a7san tanjia f marrakech?"
  • Output: "وعليكم السلام أ خاي! أحسن طنجية مراكشية تلقاها فجامع الفنا حدا الفرناتشية القدام، ولا عند الحج مصطفى حدا سوق السمارين. كطيب على الرماد الهادي وكتكون معلكة ومعطرة بالكمون والحامض مصير!"

6. 🏛️ Civic & Administrative Guidance (Moroccan Idara)

Provides clear, step-by-step instructions for civic and administrative procedures without bureaucratic jargon:

  • CNIE Renewal: Step-by-step documents needed (شهادة السكنى, عقد الازدياد, التمبر 75 درهم, البوابة cnie.ma).
  • Biometric Passport: Fee breakdown (تمبر 500 درهم), passeport.ma portal workflow, and consular submission details.
  • Driving License & Health Coverage: NARSA appointment guidelines and transition from RAMED to AMO تضامن.

7. 🗺️ Regional Dialect Persona Steering

Allows applications to steer lexical and phonetic choices towards specific regions of Morocco via prompt tags:

  • [جهة: الشمال] (Tangier, Tetouan): شني كتعمل أ خاي؟ مزيون بزاف، فالحافة قبالت البحر.
  • [جهة: الشرق] (Oujda, Berkane): واش راك دير أ خويا؟ رانا غايا والحمد لله، كاش جديد؟
  • [جهة: مراكش] (Marrakesh, South): الله يحييك أ سيدي البهجاوي! هانية والوقت زوينة، النزاهة والطنجية فالفرناتشي.
  • [جهة: سوس] (Souss, Agadir): أزول فلاون! إيميك سيميك، أملو بلدي بزيت أركان وعسيلة حرة.

🗂 Training Data & Provenance

The model was fine-tuned on 54,518 deduplicated, dialect-stratified Moroccan instruction-response samples synthesized from 10 reputable sources:

Source Corpus Type Samples Focus Area
imomayiz/darija-english Parallel Translation 10,000 Bidirectional Darija $\leftrightarrow$ English conversational pairs
JasperV13/MoroccanHistory-QA Historical QA 3,200 Moroccan dynasties, treaties, rulers, and historic landmarks
bourbouh/moroccan-darija-youtube-subtitles Spoken Colloquial 8,500 Natural Moroccan dialogues, slang, and modern idioms
alielfilali01/Darija-Stories Cultural Folklore 4,100 Narrative storytelling, cultural proverbs, and tales
atlasia/DODa Lexical Semantics 9,800 Word definitions, colloquial verb conjugations, and idioms
atlasia/Moroccan-Darija-Wikipedia Encyclopedia QA 6,400 Geography, science, and world knowledge in Moroccan Darija
BounharAbdelaziz/darija_alpaca General Instruction 5,200 Moroccan reasoning, roleplay, and multi-step tasks
DrIAmed/darija-youtube-dataset Dialogue Transcripts 3,800 Everyday social banter, Moroccan customer service scenarios
Synthetic Moroccan Seed Generator Cultural Synthesis 3,518 Traditional cuisine, craftsmanship, proverbs, and manners
Total Cleaned & Normalized 54,518 Stratified across train (46.3k), val (5.4k), test (2.7k)

All data was normalized to remove noisy Unicode artifacts, deduplicated using Jaccard token overlap ($\ge 0.85$), and formatted into Gemma 2 chat turns.

🔬 Technical Specifications

Architecture & Training Hyperparameters 

Base Model: silma-ai/SILMA-Kashif-2B-Instruct-v1.0 (Gemma 2 Architecture)
Base Parameters: 2,614,341,888 (~2.6B)
Trainable Parameters: 19,841,024 (LoRA, ~0.76% of model)
Adapter Checkpoint Size: 79.3 MB

LoRA Configuration:
  r (rank): 16
  lora_alpha: 32
  lora_dropout: 0.05
  bias: none
  target_modules:
    - q_proj
    - k_proj
    - v_proj
    - o_proj
    - gate_proj
    - up_proj
    - down_proj

Training Regime:
  Optimizer: AdamW (betas=[0.9, 0.999], weight_decay=0.01)
  Learning Rate: 2e-4 with linear warmup (0.05 ratio)
  Precision: Mixed FP16 / BF16
  Batch Size: 2 per device (Gradient Accumulation: 8, effective batch 16)
  Max Sequence Length: 512 tokens
  Loss Function: Cross-Entropy with Prompt Masking (labels = -100 for user turns)

⚠️ Limitations & Nuances

  1. Regional Variation: Moroccan Darija encompasses distinct regional varieties (Chamal/Northern, Casa/Chaouia, Marrakech, Souss, and Oriental/Oujda). The training distribution is primarily centered on Central and Chamal varieties; certain regional idioms may produce alternate phrasings.
  2. Orthographic Fluidity: Darija lacks a single standardized orthographic body; words may be spelled phonetically in multiple ways (e.g. ديال vs د). The model is resilient to spelling variance, but consistency may vary.
  3. Specialized Technical Knowledge: While culturally grounded, for specialized medical or legal inquiries the model should be augmented with retrieval-augmented generation (RAG).
  4. Code-Switching: Daily Moroccan speech incorporates French and Spanish loanwords. The model handles common loanwords ("طوموبيل", "رانديڤو", "كوزينة"), but heavily code-switched sentences may occasionally prompt responses in standard French or Arabic.

📜 Ethical Considerations & Cultural Preservation

This model is released openly to advance North African NLP and linguistic diversity in AI. Moroccan Darija is spoken by over 35 million people yet remains historically underrepresented in modern language models. Our objective is to democratize high-quality, open-source dialect intelligence for education, cultural preservation, and community tools.

🇲🇦 اللهم انفعنا بما علمتنا وعلمنا ما ينفعنا وزدنا علماً
"May this effort benefit the community, honor Moroccan cultural heritage, and bridge the digital divide for Maghrebi Arabic dialects."

❓ Frequently Asked Questions (FAQ)

What is SILMA-2B-Darija?

SILMA-2B-Darija is a lightweight, 2.6B-parameter instruction model fine-tuned for Moroccan Arabic (Darija / ary) and Arabizi. It is built on silma-ai/SILMA-Kashif-2B-Instruct-v1.0 (Gemma 2 2B) and fine-tuned using a 79 MB LoRA adapter trained on 54,518 Moroccan instruction-response pairs.

How does the model handle Arabizi?

The model natively understands and outputs Arabizi (Moroccan Latin script using numbers 3 for ع, 7 for ح, 9 for ق, 5 for خ). It can converse directly in Arabizi or translate between Latin numerals and Arabic script without external transliteration tools.

What are the hardware requirements?

  • 4-bit Quantization (GGUF / BitsAndBytes): Under 3 GB VRAM / RAM, runnable on standard laptops, MacBooks (Apple Silicon MPS), and edge devices.
  • 16-bit Precision: ~5.2 GB VRAM.
  • Inference speeds reach 35+ tokens/second on Apple Silicon or standard consumer GPUs.

How was the model evaluated?

The model was tested on the 100-question DarijaBench-100 benchmark across 5 core domains (history, geography, cuisine, proverbs, and administrative guidelines). It achieved a 2.77% Moroccan lexical marker density and an 85.0% proverb recall rate.

How does it differ from the base model?

The base foundation model frequently defaulted to Modern Standard Arabic (MSA) or Gulf idioms when prompted in colloquial Darija. SILMA-2B-Darija keeps generations anchored in Moroccan sentence structures and vocabulary (ديال, بزاف, كيداير, دابا, شنو, واخا).

How can developers run it locally?

You can load the LoRA adapter with Hugging Face transformers + peft, run 1-click execution with Ollama using the provided Modelfile (dist/ollama), or use pre-quantized GGUF files with LM Studio and llama.cpp.

🙏 Acknowledgements

This project builds upon exceptional open-source contributions. We extend our heartfelt gratitude and full credit to the following teams, authors, and contributors:

🏛️ Foundational Datasets & Corpora

Project Contribution
AtlasIA / DODa Team Creating and open-sourcing the foundational Moroccan Darija lexical corpus (atlasia/DODa), which serves as a cornerstone for dialectal linguistic anchors
AtlasIA / Moroccan-Darija-Wikipedia High-quality encyclopedia knowledge base in Darija used for cultural and geographic grounding
imomayiz / darija-english Parallel conversational translation pairs forming the core of dialogue training
JasperV13 / MoroccanHistory-QA Curated Moroccan historical QA pairs covering dynasties, treaties, and rulers
bourbouh / moroccan-darija-youtube-subtitles Authentic spoken Darija subtitle dialogues providing natural colloquial diversity
alielfilali01 / Darija-Stories Moroccan folklore narratives and cultural storytelling traditions
BounharAbdelaziz / darija_alpaca Moroccan instruction-following and reasoning data
DrIAmed / darija-youtube-dataset Moroccan conversational transcripts for customer service and social banter

🎙️ Speech & Neural Voice Foundations

Project Contribution
SWivid / F5-TTS Team Non-autoregressive Flow Matching DiT (Diffusion Transformer) voice architecture powering our 24kHz Darija voice cloning engine
Habibi-TTS Authors Pioneering dialectal Arabic speech synthesis foundation models and speaker identity presets
DVoice / Mozilla Common Voice Morocco Open speech data powering our ASR and prosody intonation research

🤖 Foundation Models & Architecture

Project Contribution
SILMA AI Developing the SILMA-9B-Instruct-v1.0 and SILMA-Kashif-2B-Instruct-v1.0 Arabic foundation backbones
Google / Gemma 2 Team Efficient and performant Gemma 2 architecture

👥 Community Contributors & Testers

Contributor Role
@Muno459 Special thanks for invaluable community testing, quality feedback, and validation of Moroccan Darija voice and speech outputs
AITheChillGuy Moroccan Darija Llama-3 model benchmarks and comparative evaluation
GemMaroc Community Pioneering explorations in Darija LLM fine-tuning on Qwen 2.5 architecture

🛠️ Open-Source Ecosystem

Tool Contribution
Hugging Face transformers, peft, datasets, and Hub infrastructure
Tim Dettmers / BitsAndBytes 4-bit NF4 quantization enabling accessible sub-7 GB VRAM fine-tuning
vLLM Team High-throughput low-latency inference serving
Ollama / llama.cpp Team Quantized local inference on edge devices

بارك الله فيكم جميعاً — May God bless everyone who contributed to preserving and advancing Moroccan Arabic (Darija) in open AI research.

📚 Citation & Attribution

If you use this model, the dataset pipeline, or the evaluation framework in your research, please cite:

@misc{derej_llm_silma_2026,
  title        = {SILMA-2B-Darija: A Culture-Grounded Fine-Tuned LLM for Moroccan Darija},
  author       = {Naouri, Abdelrhafar and the DerejLLM Team},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/abdnaouri/silma-darija-kashif-2b-lora}},
  note         = {Fine-tuned LoRA adapter on silma-ai/SILMA-Kashif-2B-Instruct-v1.0}
}
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