qwen_chatbot

Qwen-Chatbot: Instruction-Tuned Educational Conversational Model

Overview

Qwen-Chatbot is a fine-tuned large language model designed for educational conversational use cases. The model was trained to provide structured, clear, and context-aware responses, with emphasis on explanation quality and instructional dialogue rather than open-ended creativity.

Task

• Educational chatbot interaction
• Question answering with explanations
• Conversational tutoring assistance

Training Details

• Base model: Qwen family language model
• Fine-tuning approach: Instruction tuning
• Dataset: Custom-curated conversational instruction data
• Training environment: Single-GPU setup
• Objective: Improve response clarity, coherence, and instructional usefulness

Intended Use

This model is intended for academic research, educational chatbots, and experimentation with conversational AI systems. It is not designed for deployment in production or safety-critical environments.

Limitations

• The model has not been evaluated using standard conversational benchmarks.
• Responses may be verbose for simple queries.
• Hallucinations may occur for out-of-domain or ambiguous prompts.

Notes

• This model was developed as part of MSc-level academic coursework.
• The focus was on conversational structure and pedagogical clarity.
• Future work may include dataset expansion and formal quantitative evaluation.

Changelog

• August 2025: Initial fine-tuned release
• Planned: Improved instruction dataset and evaluation metrics

Example Usage

from transformers import pipeline

chatbot = pipeline("text-generation", model="ushasree2001/qwen_chatbot")
chatbot("How to find the factors of a number?")