LFM2-1.2B-RAG-ARABIC-AdaLoRA

LFM2-1.2B-RAG Arabic (AdaLoRA Fine-tuned)

Fine-tuned version of LiquidAI/LFM2-1.2B-RAG for Arabic reading comprehension and question answering tasks using AdaLoRA (Adaptive Low-Rank Adaptation) technique.

🏆 Performance

Arabic Broad Benchmark (ABB) - Local Evaluation

Evaluated using the official ABB benchmark evaluation script on RAG QA category:

Performance Context:

Comparing with publicly reported scores from the ABL Leaderboard "🏅 Top by Skill → RAG QA" section:

Key Achievement: Competitive RAG performance with only 1.2B parameters - significantly smaller than most comparable models, making it ideal for edge deployment and resource-constrained environments.

Note: This is a local evaluation. The official leaderboard submission has not been made yet.

📋 Model Description

This model specializes in extractive question answering for Arabic text with adaptive parameter allocation. It has been fine-tuned on the Arabic Reading Comprehension Dataset (ARCD) using AdaLoRA, which dynamically adjusts the rank of different layers during training for optimal performance.

Key Features:

• Optimized for Arabic extractive QA with adaptive rank allocation
• Context-based question answering with high faithfulness
• Balanced performance across multiple evaluation metrics
• Parameter-efficient fine-tuning via AdaLoRA

🎯 Intended Use

Direct Use

• Arabic question answering systems
• RAG (Retrieval-Augmented Generation) applications for Arabic content
• Information extraction from Arabic documents
• Educational tools for Arabic reading comprehension
• Chatbots requiring grounded Arabic responses

Downstream Use

Can be further fine-tuned for:

• Domain-specific QA (medical, legal, financial)
• Multi-turn conversational QA
• Cross-lingual QA systems
• Document analysis pipelines

Out-of-Scope Use

Not recommended for:

• Open-domain question answering without context
• Creative writing or story generation
• Machine translation
• Code generation or technical programming tasks

🚀 How to Use

Basic Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_id = "azeddinShr/LFM2-1.2B-RAG-ARABIC-AdaLoRA"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Prepare input
context = "نيوم هو مشروع ضخم في شمال غرب السعودية بتكلفة 500 مليار دولار."
question = "ما هي تكلفة مشروع نيوم؟"

prompt = f"استخدم السياق التالي للإجابة على السؤال:\n\n{context}\n\nالسؤال: {question}"

# Generate answer
messages = [{"role": "user", "content": prompt}]
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        input_ids,
        max_new_tokens=150,
        temperature=0.0,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id
    )

answer = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
print(answer)  # Output: 500 مليار دولار

📊 Training Details

Training Data

• Dataset: hsseinmz/arcd
• Training samples: 693
• Validation samples: 351
• Test samples: 351
• Language: Modern Standard Arabic
• Task: Extractive question answering

Training Procedure

Fine-tuning method: AdaLoRA (Adaptive Low-Rank Adaptation)

Hyperparameters:

• Base model: LiquidAI/LFM2-1.2B-RAG
• Epochs: 10
• Batch size: 16 (4 per device × 4 gradient accumulation)
• Learning rate: 2e-4
• Optimizer: AdamW (8-bit paged)
• LR scheduler: Cosine
• Warmup steps: 50
• Weight decay: 0.01

AdaLoRA Configuration:

• Initial rank (r): 16
• Target average rank: 8
• Initial adapter rank: 12
• LoRA alpha: 32
• LoRA dropout: 0.05
• Pruning start step (tinit): 10% of total steps
• Pruning end step (tfinal): 70% of total steps
• Pruning frequency (deltaT): 10 steps
• Importance smoothing (beta1, beta2): 0.85
• Orthogonality regularization: 0.5
• Target modules: w1, w2, w3, q_proj, k_proj, v_proj, out_proj, in_proj

Training infrastructure:

• Precision: bfloat16
• Gradient checkpointing: Enabled
• Framework: Hugging Face Transformers + PEFT + TRL

🔒 Ethical Considerations

• This model should not be used to generate misleading information or propaganda
• Outputs should be fact-checked for critical applications
• The model reflects statistical patterns in training data and may not represent complete or unbiased knowledge
• Users are responsible for ensuring appropriate use in their applications

🔬 Technical Details

What is AdaLoRA?

AdaLoRA (Adaptive Low-Rank Adaptation) extends LoRA by dynamically allocating parameter budgets across different weight matrices based on their importance during training. This results in:

• More efficient parameter usage
• Better performance with fewer trainable parameters
• Automatic pruning of less important adaptations

Advantages over standard LoRA

• Adaptive rank allocation based on importance scores
• Better performance-efficiency trade-off
• More stable training dynamics

📜 Citation

If you use this model in your research or application, please cite:

@misc{lfm2-arabic-qa-adalora,
  author = {Azeddin Sahir},
  title = {LFM2-1.2B-RAG Arabic (AdaLoRA Fine-tuned)},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/azeddinShr/lfm2-1.2b-arabic-qa-adalora}}
}

👍🏻 Acknowledgments

• Base Model: LiquidAI for LFM2-1.2B-RAG
• Dataset: ARCD - Arabic Reading Comprehension Dataset
• Framework: Hugging Face Transformers, PEFT, TRL
• Method: AdaLoRA by Zhang et al.

📄 License

Base model License

📧 Contact

For questions, issues, or collaboration opportunities, please open an issue in the model repository, contact via Hugging Face, or email me directly at [email protected].

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Note: This is a research model. Always validate outputs for your specific use case and domain.