saher3/ml-assistant

Arabic Machine Learning Assistant (Mistral-7B + QLoRA)

Overview

This model is a domain-specific fine-tuned version of Mistral-7B, optimized for generating clear and structured explanations of Machine Learning concepts in Arabic.

The model leverages parameter-efficient fine-tuning (LoRA) combined with 4-bit quantization (QLoRA) to achieve strong performance while maintaining computational efficiency.

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Key Capabilities

• Generates structured explanations in Arabic
• Provides simplified breakdowns of complex ML concepts
• Produces consistent outputs using a defined format:
  • Definition
  • Example
  • Analogy

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Training Methodology

Base Model: Mistral-7B
Fine-Tuning Approach: LoRA (Low-Rank Adaptation)
Quantization: 4-bit (QLoRA - nf4, double quantization)
Training Type: Instruction Tuning

The model was trained on a custom-curated Arabic dataset focused on Machine Learning explanations, emphasizing clarity, structure, and real-world understanding.

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Example

Input

اشرح Overfitting

Output

Definition: ...

Example: ...

Analogy: ...

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Performance Improvement

Before Fine-Tuning:

• Generic and unstructured responses
• Occasional prompt repetition
• Limited clarity in explanations

After Fine-Tuning:

• Structured and consistent responses
• Improved conceptual understanding
• Clear Arabic explanations tailored for learning

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Intended Use Cases

• Educational tools for Arabic-speaking learners
• AI-powered assistants for ML explanations
• Content generation for technical topics in Arabic

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Limitations

• Primarily optimized for Machine Learning topics
• Arabic responses are more refined than English
• May occasionally produce repetitive phrasing

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Technical Notes

• Fine-tuned using PEFT for memory efficiency
• Designed to run with quantization-aware setups
• Can be deployed on limited-resource environments

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("saher3/ml-assistant")
tokenizer = AutoTokenizer.from_pretrained("saher3/ml-assistant")