PraneetNS/EduMentor-Qwen3-4B-FP16

TEXT GENERATIONConcurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jun 20, 2026Architecture:Transformer0.0K Featherless Exclusive Cold

EduMentor-Qwen3-4B-FP16 is a 4 billion parameter, fine-tuned Qwen3 model developed by PraneetNS, specifically designed as an AI engineering mentor. It is optimized for speech-to-speech AI systems, separating spoken responses from visual artifacts like code and diagrams. This model excels at providing real-time conversational learning, coding assistance, and career guidance across various engineering domains.

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EduMentor-Qwen3-4B-FP16: An AI Engineering Mentor

EduMentor is a specialized 4 billion parameter model, fine-tuned from Qwen3, to function as an AI engineering mentor. Its core innovation lies in its optimization for speech-to-speech AI systems, enabling it to deliver natural spoken explanations while simultaneously generating structured visual artifacts such as code, diagrams, and roadmaps. This unique capability prevents the common issue of AI reading code or large tables aloud, enhancing the conversational learning experience.

Key Capabilities

  • Speech-to-Speech Optimized Responses: Delivers structured JSON outputs with separate fields for speech, display (code, diagram, roadmap, notes), and follow_up questions, ideal for voice assistants and multimodal agents.
  • Broad Engineering Mentoring: Provides guidance across diverse fields including Computer Science (programming, algorithms, system design), Artificial Intelligence (ML, DL, LLMs, RAG), Electronics/ECE, Mechanical Engineering, and Civil Engineering.
  • Career Mentoring: Assists students with placement preparation, internship planning, resume improvement, project ideas, and interview readiness.
  • Fine-tuning Details: Utilizes LoRA SFT with a rank of 32, focusing on natural mentor conversations, engineering explanations, code artifact separation, emotional support, and multi-domain knowledge.

Good For

  • Real-time AI Tutors: Ideal for applications requiring interactive, conversational learning with visual aids.
  • Multimodal Agents: Supports integration with systems that combine voice interaction with visual content display.
  • Engineering Education Platforms: Enhances learning by providing structured, domain-specific guidance.
  • Further Fine-tuning: The FP16 version is recommended for additional fine-tuning or high-quality inference via vLLM deployment.

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