license, license_name, license_link, base_model, tags, language, datasets, library_name, pipeline_tag, model-index

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

Qwen3-4B Customer Support Fine-Tuned Model

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.

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:

# 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