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HoangPhu/
ticket-classifier-qwen25-7b-fp16

Name: ticket-classifier-qwen25-7b-fp16
Availability: InStock

safetensors

qwen2

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IT Ticket Classifier - Qwen2.5 7B FP16 Quantized

Model Details

Base Model: unsloth/Qwen2.5-7B-Instruct
Fine-tuning: LoRA adapter merged
Quantization: FP16 (Half Precision)
Date: 2026-06-05 11:00:29
Original Accuracy: 78.72%
Expected Accuracy (FP16): 78.6-78.7% (99% retained)

File Size

Original (FP32): ~15 GB
Quantized (FP16): ~7.5 GB
Compression: 50%

Usage with Ollama

Method 1: From Hugging Face (Recommended)

bash

# Clone the repository locally
git clone https://huggingface.co/HoangPhu/ticket-classifier-qwen25-7b-fp16
cd ticket-classifier-qwen25-7b-fp16

# Create Ollama model
ollama create ticket-classifier-fp16 -f Modelfile

# Test the model
ollama run ticket-classifier-fp16 "My VDI is not connecting"

Method 2: Using Ollama API

python

import requests

url = "http://localhost:11434/api/generate"
payload = {
    "model": "ticket-classifier-fp16",
    "prompt": "My laptop is slow",
    "stream": False
}

response = requests.post(url, json=payload)
print(response.json()['response'])

Service Categories

The model classifies tickets into 21 categories:

Account Troubleshooting
Application Troubleshooting
Client Troubleshooting
Create New VDI
Deploy Application
Enable VDI
Endpoint Security
FPT Chat Deployment
ISMS Support
Infrastructure Support
Install/Remove Software
Network Port Configuration
Network Troubleshooting
Office Installation
Other / Manual Review
PASSWORD_RESET
Register Application Entra ID
Remove VDI
Reset MFA O365
Upgrade VDI
VDI Troubleshooting

Model Performance

Accuracy: 78.72%
High-Confidence (≥30%) Accuracy: 100%
Recommended Production Threshold: 30%

Deployment Strategy

Recommended: Hybrid Confidence-Based Routing

If confidence >= 30%:
  → Auto-route to service team
Else:
  → Manual review required

This ensures high accuracy for auto-routed tickets while maintaining oversight for uncertain cases.

Requirements

Ollama (v0.1.0+)
At least 16 GB RAM for CPU inference
Or GPU (recommended for faster inference)

Inference Speed

CPU (Intel i7): 5-10 seconds per ticket
GPU (NVIDIA): 0.5-2 seconds per ticket

License

Same as source model (Qwen2.5-7B-Instruct)

Quantization Details

This model was converted from FP32 (32-bit float) to FP16 (16-bit float):

Minimal quality loss (~1%)
50% size reduction
Faster loading times
Compatible with most hardware