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Model: ragib01/Qwen3-4B-customer-support Source: Original Platform
2026-06-16 01:22:18 +08:00
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+---
+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