New in Version 2
Compared to EduMentor v1, this release includes:
- Fine-tuned on an expanded multi-turn engineering conversation dataset (~27K conversations).
- Improved contextual follow-up handling.
- Better conversational flow for tutoring sessions.
- Stronger identity consistency as EduMentor.
- Improved reasoning across Computer Science and core engineering subjects.
- Enhanced placement and career guidance.
- Better structured JSON responses for multimodal applications.
- Verified merged FP16 checkpoint (no LoRA dependency).
Table with columns: Property, Value| Property | Value |
|---|
| Base Model | EduMentor-Qwen3-4B-FP16 (v1) |
| Architecture | Qwen3-4B |
| Fine-tuning | Supervised Fine-Tuning (LoRA) |
| Merge | Fully merged FP16 |
| Context Length | 4096 tokens |
| Precision | FP16 |
| Intended Use | Engineering Mentor |
Training Dataset
EduMentor v2 was trained on approximately 27,000 carefully curated multi-turn conversations covering engineering education.
The dataset emphasizes:
- realistic mentor-student interactions
- conceptual teaching
- problem solving
- project guidance
- interview preparation
- career mentoring
- emotional encouragement
- structured responses
The conversations include contextual follow-up questions to simulate natural tutoring sessions.
Supported Domains
Computer Science
- Programming Fundamentals
- Object Oriented Programming
- Data Structures
- Algorithms
- Operating Systems
- DBMS
- Computer Networks
- Software Engineering
Artificial Intelligence
- Machine Learning
- Deep Learning
- Neural Networks
- LLMs
- Transformers
- RAG
- Prompt Engineering
- AI Deployment
Electronics
- Digital Electronics
- Analog Electronics
- Signals
- Communication
- Embedded Systems
- Microprocessors
Electrical Engineering
- Machines
- Power Systems
- Control Systems
- Power Electronics
Mechanical Engineering
- Thermodynamics
- Manufacturing
- Design
- Strength of Materials
- Fluid Mechanics
Civil Engineering
- RCC
- Structural Engineering
- Surveying
- Transportation
- Environmental Engineering
Mathematics
- Calculus
- Linear Algebra
- Probability
- Statistics
- Discrete Mathematics
Career Guidance
- Placements
- Resume Reviews
- Internship Guidance
- Interview Preparation
- Learning Roadmaps
- Project Ideas
EduMentor is designed for multimodal tutoring systems.
Typical responses follow the format:
{
"speech": "...",
"display": {
"type": "code | notes | roadmap | table | flowchart",
"content": "..."
},
"follow_up": "..."
}
This allows downstream applications to:
- speak only the explanation
- display diagrams separately
- avoid reading code aloud
- render structured educational artifacts
Intended Voice Pipeline
User Speech
│
▼
Speech Recognition
│
▼
EduMentor v2
│
▼
JSON Parser
│
┌────┴─────────────┐
▼ ▼
Speech Display
(TTS) (Code / Notes / Roadmaps)
Recommended stack:
- Faster-Whisper
- EduMentor
- llama.cpp
- Kokoro TTS
Example
User
Explain Binary Search.
Assistant
{
"speech": "Binary Search repeatedly divides the search space in half, making it much faster than linear search on sorted data.",
"display": {
"type": "code",
"language": "python",
"content": "def binary_search(...): ..."
},
"follow_up": "Would you like to understand the time complexity?"
}
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "PraneetNS/EduMentor-Qwen3-4B-v2-FP16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto"
)
Intended Applications
EduMentor is suitable for:
- AI Tutors
- Educational Chatbots
- Voice Assistants
- Engineering Learning Platforms
- Placement Preparation
- Career Mentoring
- Project Guidance
- Classroom Assistants
Limitations
EduMentor may occasionally:
- generate incorrect technical information
- require verification for safety-critical engineering tasks
- produce imperfect JSON formatting for highly complex requests
- benefit from external tools or retrieval for rapidly changing topics
It should not be considered a replacement for certified professional engineering advice.
Roadmap
Future versions aim to include:
- Tool Calling
- Retrieval-Augmented Generation (RAG)
- Long-Term Student Memory
- Personalized Learning Plans
- Multimodal Diagram Generation
- Real-Time Coding Assistance
- Agentic Workflows
Citation
If you use EduMentor in academic work or projects, please cite this repository.
Acknowledgements
EduMentor is built upon the Qwen3 architecture and fine-tuned to provide personalized engineering education through conversational AI.
Creator
Praneet N S
EduMentor is an ongoing effort to build an AI mentor capable of assisting engineering students through natural conversations, structured explanations, and voice-first educational experiences.