---
license: apache-2.0
language:
- en
base_model:
- unsloth/Qwen3-4B-Instruct-2507
pipeline_tag: text-generation
tags:
- agent
- cex
- contect_centre
- customer_support
- telecom
---
# 🧠 Qwen3-4B-Instruct Coaching Customer Support Model

**Base Model:** [unsloth/Qwen3-4B-Instruct-2507](https://huggingface.co/unsloth/Qwen3-4B-Instruct-2507) 
**License:** Apache-2.0  
**Libraries Used:** `unsloth`, `trl`, `transformers`, `torch`, `datasets`, `peft`, `accelerate`, `bitsandbytes`

---

## 📘 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  

---
## 🧩 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  

---

## 🧠 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.. |

---

## ⚙️ Example Usage

```python
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},
}