mdk615661/it-helpdesk-qlora-v4

IT Helpdesk QLoRA Adapter — v4

LoRA adapter for IT helpdesk ticket classification. Load this adapter on top of mdk615661/it-helpdesk-merged-v3.

Model Details

• —Type: QLoRA Adapter (PEFT)
• —Base Model: mdk615661/it-helpdesk-merged-v3
• —LoRA: r=16, alpha=32
• —Training Data: 2,000 IT helpdesk records
• —Training Loss: 0.187 (3 epochs)
• —Hardware: Kaggle T4 GPU (33 min)

Usage

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.

Categories

Version History

Training Hyperparameters

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