---
base_model: arcee-ai/Meraj-Mini
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- ar
- en
---
![image](./image.webp)
# Bilingual Assistant Model Card

## Overview

This bilingual language model is designed to support seamless text generation and understanding in both Arabic (ar) and English (en). Fine-tuned from the `arcee-ai/Meraj-Mini` base model, it offers robust multilingual capabilities optimized for various applications such as conversational agents, content creation, and multilingual text analysis.

### Key Highlights

- **Multilingual Proficiency:** Designed to handle complex linguistic nuances in both Arabic and English, ensuring high-quality outputs in both languages.
- **Performance Optimization:** Achieved 2x faster training through innovative methods provided by the [Unsloth](https://github.com/unslothai/unsloth) framework and the Hugging Face TRL library.
- **Transformer-Based Architecture:** Utilizes advanced transformer layers to deliver state-of-the-art performance in text generation and inference.

## Development Details

- **Developer:** Daemontatox
- **License:** Licensed under the Apache-2.0, ensuring open accessibility and flexibility for various use cases.
- **Base Model:** The model is a fine-tuned variant of `arcee-ai/Meraj-Mini`.
- **Frameworks Used:**
  - [Unsloth](https://github.com/unslothai/unsloth): Enabled faster and more efficient training.
  - Hugging Face TRL Library: Provided tools for reinforcement learning fine-tuning, enhancing model responsiveness and accuracy.

## Training Process

The fine-tuning process was conducted with a focus on:

- **Data Diversity:** Leveraged a bilingual corpus to ensure comprehensive language understanding across both supported languages.
- **Optimized Hardware Utilization:** Implemented Unsloth's accelerated training methods, significantly reducing resource consumption and training time.
- **Reinforcement Learning:** Used Hugging Face's TRL library to fine-tune the model's decision-making and response generation capabilities, particularly for conversational and contextual understanding.

## Applications

This model is suited for a variety of real-world applications, including:

1. **Conversational Agents:** Powering bilingual chatbots and virtual assistants for customer support and personal use.
2. **Content Generation:** Assisting in drafting multilingual articles, social media posts, and creative writing.
3. **Translation Support:** Providing context-aware translations and summaries across Arabic and English.
4. **Education:** Enhancing learning platforms by offering bilingual educational content and interactive learning experiences.

## Future Directions

Plans for extending the model's capabilities include:

- **Additional Language Support:** Exploring fine-tuning for additional languages.
- **Domain-Specific Training:** Specializing the model for industries such as healthcare, legal, and technical writing.
- **Optimization for Edge Devices:** Investigating quantization techniques to deploy the model on resource-constrained hardware like mobile devices and IoT platforms.