from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "mdk615661/it-helpdesk-merged-v4"
tokenizer  = AutoTokenizer.from_pretrained(model_name)
model      = AutoModelForCausalLM.from_pretrained(
    model_name,
    dtype=torch.float16,
    device_map="auto"
)

prompt = """### Instruction:
Normalize and classify this IT helpdesk ticket.

### Input:
Laptop is not turning on

### Output:
"""

inputs  = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    do_sample=False,
    repetition_penalty=1.3,
    pad_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Output Format

Category: Hardware SubCategory: Hardware - Laptop Normalized: laptop not working Priority: Medium Insight: Hardware failure preventing user from working. Recommendation: Raise repair request with IT hardware team.

Categories

Category	Subcategories
Hardware	Laptop, Charger, Mobile
Software	Installation, VPN, Password Reset, O365, Teams Issue, MFA Reset, BitLocker, OS Installation, USB Access
Incident	Critical Incident, Network Outage, Security Incident, Service Outage, Performance Incident
Procurement	Hardware Procurement, Software Procurement
Onboarding & Offboarding	Onboarding, Offboarding
Cloud & Infrastructure	DR and BCP, Infrastructure Configuration, Network Configuration Request, System Configuration
Asset	Asset Management, Asset Request, Asset - Client
Others	Account Management, Audit and Compliance, Change Management, IT Training Request, Vendor Support

Training Hyperparameters

Parameter	Value
Epochs	3
Batch size	4
Gradient accumulation	4
Learning rate	2e-4
Warmup steps	50
LR scheduler	cosine
LoRA r	16
LoRA alpha	32

Version History

Version	Training Data	Final Loss
v3	1,141 real TruMIS tickets	—
v4 (this)	+ 2,000 Qwen records	0.187