---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
base_model: unsloth/Qwen3-4B-Instruct-2507
tags:
  - customer-support
  - chatbot
  - conversational-ai
  - tool-calling
  - entity-extraction
  - unsloth
  - qwen
  - trl
  - sft
language:
  - en
  - multilingual
datasets:
  - bitext/Bitext-customer-support-llm-chatbot-training-dataset
library_name: transformers
pipeline_tag: text-generation
model-index:
  - name: Qwen3-4B-customer-support
    results: []
---
# Qwen3-4B Customer Support Fine-Tuned Model

This is a fine-tuned version of [Qwen3-4B-customer-support](https://huggingface.co/ragib01/Qwen3-4B-customer-support) specifically optimized for customer support interactions. The model has been trained to handle common customer service scenarios including order tracking, refunds, invoice management, and general inquiries.

<a href="https://chat.qwen.ai" target="_blank" style="margin: 2px;">
    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>

## Model Description
  
-  **Base Model:** unsloth/Qwen3-4B-Instruct-2507

-  **Fine-tuning Method:** QLoRA (4-bit quantization with LoRA adapters)

-  **Training Framework:** Unsloth + TRL

-  **Parameters:** 4B (4,055,498,240 total parameters)

-  **Trainable Parameters:** 33,030,144 (0.81% of total)

-  **Language Support:** English + Multilingual capabilities from base model

## Key Features

✅ **Tool-Calling Capability** - Trained to use structured tool calls for data retrieval (order tracking, invoice lookup, refund processing)

✅ **Entity Extraction** - Accurately extracts and preserves values like order numbers, dates, email addresses, and account information

✅ **Multilingual Support** - Inherits multilingual capabilities from Qwen3 base model  

✅ **Memory Efficient** - Trained with 4-bit quantization and LoRA adapters

✅ **MCP Compatible** - Architecture preserved for Model Context Protocol compatibility


## Usage

### Basic Inference
The following contains a code snippet illustrating how to use the model generate content based on given inputs.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "ragib01/Qwen3-4B-customer-support",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "ragib01/Qwen3-4B-customer-support",
    trust_remote_code=True
)

# Test with a customer support query
messages = [
    {"role": "system", "content": "You are a helpful customer support assistant."},
    {"role": "user", "content": "How do I track my order #74758657?"}
]

# Format input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

# Generate response
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        top_p=0.9,
        do_sample=True
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Tool-Calling Support

The model can generate structured tool calls for actions requiring data retrieval:

```python
# Example: The model will generate tool calls for order tracking

user_query = "Can you check the status of order #98765432?"

# Model output will include:

<tool_call>
{
  "name": "track_order",
  "arguments": {
    "order_number": "#98765432"
  }
}
</tool_call>

```

## Use Cases

-  **Customer Support Chatbots** - Automated responses for common inquiries
-  **Order Management** - Track orders, cancel orders, modify shipping details
-  **Refund Processing** - Handle refund requests and track refund status
-  **Invoice Management** - Retrieve and explain invoice details
-  **Account Management** - Help with account-related questions
-  **Product Information** - Answer questions about products, shipping, and policies