startelelogic/Qwen3-4B-Instruct-2507-Customer-Support

🧠 Qwen3-4B-Instruct Coaching Customer Support Model

Base Model: unsloth/Qwen3-4B-Instruct-2507 License: Apache-2.0
Libraries Used: unsloth, trl, transformers, torch, datasets, peft, accelerate, bitsandbytes

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📘 Model Overview

This model is a fine-tuned version of Qwen3-4B-Instruct, specialized for educational support and institute FAQs.
It helps provide structured and factual answers about JEE and NEET coaching programs, batches, fees, and facilities.

Key Abilities:

• Answers academic and course-related questions
• Explains coaching programs and structures
• Shares institute details (fees, modes, schedules)
• Simulates realistic student–counselor chat conversations

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🧩 Fine-tuning Details

• Framework: PyTorch

• Libraries:

  • 🤗 Transformers
  • PEFT (Parameter-Efficient Fine-Tuning)
  • Unsloth (for faster fine-tuning)
  • BitsAndBytes (4-bit/8-bit quantization)
  • Accelerate
  • Datasets

• Training Objective: Instruction-following and educational support conversations

• Input Format: Alpaca/ShareGPT style

• Output Format: Chat-style assistant response

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🧠 Intended Use

Use Case	Description
📚 Educational Assistant	Answers student queries about JEE/NEET batches and programs.
💬 Chatbot Integration	Can be integrated into coaching institute websites or portals.
🧾 FAQ Automation	Ideal for handling structured question–answer interactions..

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⚙️ Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "startelelogic/Qwen3-4B-Instruct-2507-Customer-Support"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

prompt = "List all NEET programs offered by EduQuest Academy."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📝 Citation

@misc {qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, }