from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    "mdk615661/it-helpdesk-merged-v3",
    dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "mdk615661/it-helpdesk-qlora-v4")
tokenizer = AutoTokenizer.from_pretrained("mdk615661/it-helpdesk-merged-v3")

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)
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.

Version History

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

Training Hyperparameters

Epochs: 3
Batch size: 4
Gradient accumulation: 4
Learning rate: 2e-4
Warmup steps: 50
LR scheduler: cosine
Precision: fp16

Category	Subcategories
Hardware	Laptop, Charger, Mobile
Software	Installation, VPN, Password Reset, O365, Teams, MFA Reset
Incident	Critical, Network Outage, Security, Service Outage, Performance
Procurement	Hardware, Software
Onboarding & Offboarding	Onboarding, Offboarding
Cloud & Infrastructure	DR/BCP, Network Config, System Config
Asset	Asset Management, Asset Request
Others	Account Management, Audit, Change Management

mdk615661/it-helpdesk-qlora-v4

IT Helpdesk QLoRA Adapter — v4

Model Details

Usage

Output Format

Categories

Version History

Training Hyperparameters

mdk615661/
it-helpdesk-qlora-v4