Qwen3-4B Customer Support Fine-Tuned Model

Name: Qwen3-4B-customer-support
Availability: InStock

This is a fine-tuned version of 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.

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