Instructions to use PraneetNS/EduMentor-Qwen3-4B-v2-FP16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PraneetNS/EduMentor-Qwen3-4B-v2-FP16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="PraneetNS/EduMentor-Qwen3-4B-v2-FP16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PraneetNS/EduMentor-Qwen3-4B-v2-FP16")
model = AutoModelForCausalLM.from_pretrained("PraneetNS/EduMentor-Qwen3-4B-v2-FP16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PraneetNS/EduMentor-Qwen3-4B-v2-FP16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PraneetNS/EduMentor-Qwen3-4B-v2-FP16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PraneetNS/EduMentor-Qwen3-4B-v2-FP16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PraneetNS/EduMentor-Qwen3-4B-v2-FP16

SGLang

How to use PraneetNS/EduMentor-Qwen3-4B-v2-FP16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "PraneetNS/EduMentor-Qwen3-4B-v2-FP16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PraneetNS/EduMentor-Qwen3-4B-v2-FP16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "PraneetNS/EduMentor-Qwen3-4B-v2-FP16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PraneetNS/EduMentor-Qwen3-4B-v2-FP16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PraneetNS/EduMentor-Qwen3-4B-v2-FP16 with Docker Model Runner:
```
docker model run hf.co/PraneetNS/EduMentor-Qwen3-4B-v2-FP16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EduMentor Qwen3 4B v2 (FP16)

EduMentor v2 is the second major release of EduMentor, an AI engineering mentor built for university students.

The model is designed to provide conversational technical guidance, placement preparation, project mentoring, and structured educational responses across multiple engineering disciplines.

Unlike a generic chatbot, EduMentor is optimized for voice-first tutoring systems where spoken explanations are separated from visual artifacts such as code, diagrams, roadmaps, tables, and notes.

Highlights

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).

Model Information

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

Response Format

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.

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Safetensors

Model size

4B params

Tensor type

F16

Model tree for PraneetNS/EduMentor-Qwen3-4B-v2-FP16

Base model

PraneetNS/EduMentor-Qwen3-4B-FP16

Finetuned

(1)

this model

Quantizations

3 models