silma_9b_instruct_ft

SILMA-9B-Instruct Fine-Tuned for Arabic Reasoning-QA

This model is a fine-tuned version of silma-ai/SILMA-9B-Instruct-v1.0, optimized for Arabic Question Answering tasks. It excels at providing numerical answers to a wide range of questions in Arabic.

Model Descriptionen

This fine-tuned model is based on the silma-ai/SILMA-9B-Instruct-v1.0 and is designed to answer reasoning questions in Arabic, providing integer-based answers. The model has be fine-tuned using a custom Arabic Reasoning QA dataset, specifically tailored to handle questions ranging from easy to difficult across various topics.

Model Details

• Model Name: silma_9b_instruct_ft
• Model Type: Language Model
• Language: Arabic
• Base Model: silma-ai/SILMA-9B-Instruct-v1.0
• Fine-Tuning Method: PEFT with LoraConfig
• Task: Arabic Question Answering (Numerical Responses)
• Training Data: Custom Arabic Reasoning QA dataset
• Quantization: 4-bit quantization using bitsandbytes

Features

• Optimized for Arabic language understanding and generation
• Specialized in providing numerical answers to questions
• Efficient inference with 4-bit quantization
• Fine-tuned using PEFT with LoraConfig for parameter-efficient training

Training results

Training Metrics

Training Loss on wandb 🔗

Usage

Here's a quick example of how to use the model:

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "MohammedNasser/silma_9b_instruct_ft"
user_question = "إذا كان لديك ثلاث سيارات، وبعت واحدة منها، كم سيارة ستبقى لديك؟"

# Load model and tokenizer
import torch
from transformers import pipeline

# Create pipeline
pipe = pipeline(
    "text-generation",
    model=model_name,
    torch_dtype= torch.bfloat16,
    device="cuda",
    return_full_text=False, 
)

messages = [
    {"role": "user", "content": user_question },
]

# Example usage
response = pipe(messages, max_new_tokens=128)
assistant_response = outputs[0]["generated_text"]

print(f"Question: {user_question}")
print(f"Answer: {assistant_response}")

Performance

Our model demonstrates strong performance on Arabic QA tasks, particularly for questions requiring numerical answers. Here are some key metrics:

• Eval Loss: 0.046

Limitations

• The model is optimized for numerical answers and may not perform as well on open-ended questions.
• Performance may vary for dialects or regional variations of Arabic not well-represented in the training data.
• The model may occasionally generate incorrect numerical answers for very complex or ambiguous questions.

Fine-tuning Details

The model was fine-tuned using the following configuration:

• LoRA Config:
  • Alpha: 16
  • Dropout: 0.1
  • R: 4
• Training Hyperparameters:
  • Batch Size: 4
  • Learning Rate: 2e-4
  • Epochs: 3
• Hardware: 4 x NVIDIA A100 GPUs

Citation

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

@misc {gaber_2024,
	author       = { {Gaber} },
	title        = { silma_9b_instruct_ft (Revision e54c562) },
	year         = 2024,
	url          = { https://huggingface.co/MohammedNasser/silma_9b_instruct_ft },
	doi          = { 10.57967/hf/3032 },
	publisher    = { Hugging Face }
}

Made with ❤️ by [M. N. Gaber/aiNarabic]