license, language, library_name, pipeline_tag, base_model, tags

license	language	library_name	pipeline_tag	base_model	tags
apache-2.0	en	llama.cpp	text-generation	PraneetNS/EduMentor-Qwen3-4B-v2-FP16	gguf llama.cpp qwen3 engineering education mentor conversational ai

EduMentor Qwen3 4B v2 (GGUF)

This repository contains GGUF quantized versions of EduMentor Qwen3 4B v2, an AI engineering mentor designed for university students, technical learning, coding assistance, project guidance, and placement preparation.

These models are converted from the merged FP16 checkpoint and optimized for inference with llama.cpp, LM Studio, Ollama (GGUF import), Open WebUI, Jan, and other GGUF-compatible runtimes.

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Base Model

FP16 Repository

https://huggingface.co/PraneetNS/EduMentor-Qwen3-4B-v2-FP16

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Available Quantizations

File	Size	Recommended For
EduMentor-Qwen3-4B-v2-F16.gguf	~7.5 GB	Maximum quality
EduMentor-Qwen3-4B-v2-Q8_0.gguf	~4.0 GB	Near-FP16 quality
EduMentor-Qwen3-4B-v2-Q6_K.gguf	~3.1 GB	Best balance of quality and speed
EduMentor-Qwen3-4B-v2-Q4_K_M.gguf	~2.4 GB	Fast local inference

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Features

EduMentor v2 is fine-tuned for engineering education and technical mentoring.

It supports:

• Programming Fundamentals
• Data Structures & Algorithms
• System Design
• Machine Learning
• Deep Learning
• Large Language Models
• Electronics
• Electrical Engineering
• Mechanical Engineering
• Civil Engineering
• Mathematics
• Resume Reviews
• Internship Preparation
• Placement Preparation
• Interview Guidance
• Project Mentoring

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Response Format

EduMentor is optimized for multimodal tutoring systems.

Typical responses follow the structure:

{
  "speech": "...",
  "display": {
    "type": "code | notes | roadmap | table | diagram",
    "content": "..."
  },
  "follow_up": "..."
}

This enables voice assistants to speak only natural explanations while rendering code, diagrams, or notes separately.

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Example

User

Explain Binary Search.

Assistant

{
  "speech": "Binary Search repeatedly divides the search interval in half, making it much faster than linear search on sorted arrays.",

  "display": {
    "type": "code",
    "language": "python",
    "content": "def binary_search(...): ..."
  },

  "follow_up": "Would you like to understand its time complexity?"
}

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Running with llama.cpp

./llama-cli \
-m EduMentor-Qwen3-4B-v2-Q4_K_M.gguf \
-c 4096 \
-p "Explain recursion simply."

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Recommended Quantization

Hardware	Recommendation
8 GB RAM	Q4_K_M
16 GB RAM	Q6_K
24 GB+ RAM	Q8_0
GPU Servers	F16

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Limitations

EduMentor may occasionally:

• produce incorrect technical information
• require verification for safety-critical engineering tasks
• generate imperfect JSON formatting for complex prompts
• benefit from retrieval augmentation for rapidly changing topics

The model should not replace professional engineering advice.

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Training

Base Architecture:

• Qwen3-4B

Fine-tuning:

• Supervised Fine-Tuning (LoRA)
• Fully merged into FP16
• Converted to GGUF using llama.cpp

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Creator

Praneet N S

EduMentor is an ongoing effort to build an AI mentor capable of providing high-quality engineering education through natural conversations and voice-first tutoring systems.