Technical Concept Simplifier
Model Overview
Technical Concept Simplifier is a domain-adapted Large Language Model developed to transform complex technical concepts into clear, structured, and beginner-friendly explanations.
The model is fine-tuned on a curated educational dataset containing technical instruction-response pairs across multiple computer science and engineering domains. Its primary objective is to improve accessibility to technical knowledge by generating explanations that are accurate, intuitive, and easy to understand for students, beginners, and aspiring developers.
This project was developed as part of an AI model adaptation and fine-tuning initiative using the Adaption platform.
Motivation
Modern Large Language Models possess extensive technical knowledge but often provide explanations that can be difficult for beginners to understand. Technical Concept Simplifier addresses this challenge by specializing the model in educational concept simplification.
The model focuses on explaining advanced topics through:
- Plain-language explanations
- Step-by-step reasoning
- Real-world analogies
- Beginner-oriented teaching style
- Concept-focused responses
Base Model
Meta-Llama-4-Scout-17B-16E-Instruct
The model was adapted using parameter-efficient fine-tuning techniques to improve performance on educational and technical explanation tasks while preserving the general capabilities of the base model.
Training Configuration
Fine-Tuning Method
- Supervised Fine-Tuning (SFT)
- LoRA (Low-Rank Adaptation)
Training Parameters
- LoRA Rank: 64
- LoRA Alpha: 128
- Learning Rate: 1e-4
- Scheduler: Cosine
- Epochs: 3
- Warmup Ratio: 0.03
- Weight Decay: 0.02
Dataset
The model was trained using the Technical Concept Simplifier Dataset, a curated instruction-tuning dataset consisting of 1,199 educational prompt-completion pairs.
Covered Domains
- Programming Fundamentals
- Java
- Python
- Data Structures
- Algorithms
- Database Management Systems
- Operating Systems
- Computer Networks
- Software Engineering
- Cloud Computing
- Artificial Intelligence
- Machine Learning
- Distributed Systems
- Infrastructure Concepts
Dataset Repository:
Training Results
Dataset Adaptation Results
- Quality Score Improved: 6.0 → 9.2
- Relative Improvement: 53.3%
- Grade Improvement: C → A
- Percentile Improvement: 7.5 → 33.0
Fine-Tuning Performance
- Dataset Win Rate: 68%
- Code Win Rate: 60%
These results indicate that the adapted model consistently outperformed the baseline model on evaluation tasks related to the training domain.
Intended Use Cases
This model is suitable for:
- Educational AI Assistants
- Technical Tutoring Systems
- Beginner Learning Platforms
- Concept Simplification Applications
- Computer Science Learning Tools
- AI-Powered Teaching Assistants
Keywords
- Llama 4
- LoRA Fine-Tuning
- Technical Education
- Computer Science
- Artificial Intelligence
- Machine Learning
- Technical Concept Simplification
- Educational AI
Limitations
- Optimized primarily for technical education and concept explanation.
- Performance may vary on highly specialized research topics outside the training distribution.
- Responses should be reviewed when used in production or academic environments.
Author
Ujjawal Bansal
B.Tech Computer Science Engineering (AI & Analytics)
Project developed for AI model adaptation, educational AI research, and technical knowledge accessibility.
License
Apache License 2.0
