Model Card for IT Helpdesk Ticket Support Llama-2 Model

🧠 Model Summary

This model is a fine-tuned version of Meta’s Llama-2-7B-Chat trained on a synthetic IT Helpdesk Ticket dataset containing 1500 real-like corporate support tickets.
The model is designed to understand and answer IT support–related questions in natural language — such as password issues, VPN problems, hardware failures, or system access requests.

It can be used for:

Automated helpdesk assistants
FAQ retrieval and response systems
Internal IT ticket triaging tools
Smart support chatbots

📄 Model Details

Base model: meta-llama/Llama-2-7b-chat-hf
Fine-tuned by: Dharunpandi
Language(s): English
Model type: Causal language model (decoder-only Transformer)
License: Meta Llama 2 Community License
Finetuned on: Synthetic IT Helpdesk Ticket dataset
Task: Text generation / Question answering in helpdesk context
Framework: 🤗 Transformers

💡 Intended Use

✅ Direct Use

Generate concise and accurate answers to IT support–related queries.
Suggest relevant departments or assigned teams for reported issues.
Summarize or classify IT support ticket content.

🔧 Downstream Use

Integrate with IT Service Management (ITSM) tools like Jira, Freshservice, or ServiceNow.
Deploy as a chatbot or assistant inside company portals.

🚫 Out-of-Scope Use

Non-technical or non-English queries.
High-stakes decision-making without human oversight.
Retrieval of private or sensitive data.

⚠️ Bias, Risks, and Limitations

The model was trained on synthetic data, not real corporate tickets — accuracy may vary in real environments.
Some responses might include hallucinated metadata (e.g., employee names or departments).
Should not be used for security-sensitive IT operations (e.g., password resets without validation).

⚙️ Training Details

Dataset

Name: Synthetic IT Helpdesk Ticket Dataset
Format: JSON (fields include Ticket_ID, Title, Description, Department, Assigned_Team, Created_At, Updated_At)
Size: 1500 samples

Preprocessing

Combined Title and Description into unified text input.
Removed random identifiers like Ticket_ID and timestamps to reduce noise.

Training Hyperparameters

Epochs: 3
Learning rate: 1e-4
Batch size: 2
Optimizer: paged_adamw_8bit
Precision: fp16 mixed
Max steps: 20
Save strategy: epoch

🧩 Evaluation

Metrics

Qualitative evaluation via generated responses for unseen IT queries.
Example:
- User: “How do I reset my corporate email password?”
- Model Response: “You can reset your password through the corporate login portal under ‘Forgot Password?’ or contact the IT Helpdesk team.”

Observations

The model can generalize to unseen support scenarios.
May occasionally add extra fields like Department or Employee Name.

🌱 Environmental Impact (Approximation)

Hardware: NVIDIA A100 (40GB)
Training Time: ~2 GPU hours
Estimated CO₂ emissions: < 1 kg CO₂eq
Compute provider: Google Colab

🔍 How to Use the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "your-username/it-helpdesk-llama2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")

question = "How do I reset my corporate email password?"
prompt = f"Question: {question}\nAnswer concisely and helpfully:\n"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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