README.md

---
base_model: arcee-ai/Meraj-Mini
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- ar
- en
model-index:
- name: MawaredT1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: wis-k/instruction-following-eval
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 41.99
      name: averaged accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FMawaredT1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: SaylorTwift/bbh
      split: test
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 31.9
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FMawaredT1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: lighteval/MATH-Hard
      split: test
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 14.58
      name: exact match
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FMawaredT1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 11.3
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FMawaredT1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 18.68
      name: acc_norm
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FMawaredT1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 41.31
      name: accuracy
    source:
      url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FMawaredT1
      name: Open LLM Leaderboard
---
![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.


# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/Daemontatox__MawaredT1-details)!
Summarized results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/contents/viewer/default/train?q=Daemontatox%2FMawaredT1&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

|      Metric       |Value (%)|
|-------------------|--------:|
|**Average**        |    26.63|
|IFEval (0-Shot)    |    41.99|
|BBH (3-Shot)       |    31.90|
|MATH Lvl 5 (4-Shot)|    14.58|
|GPQA (0-shot)      |    11.30|
|MuSR (0-shot)      |    18.68|
|MMLU-PRO (5-shot)  |    41.31|