✈️ SkyAssist-Llama

SkyAssist-Llama is a domain-specific airline customer support large language model created by fine-tuning Meta Llama 3.2 3B Instruct using QLoRA (Quantized Low-Rank Adaptation).

The model is designed to generate professional, empathetic, and context-aware responses for common airline customer support scenarios, including flight delays, cancellations, baggage inquiries, booking modifications, refunds, and check-in assistance.

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Model Details

Property	Value
Base Model	Meta Llama 3.2 3B Instruct
Fine-tuning Method	QLoRA
Task	Airline Customer Support
Language	English
Architecture	Decoder-only Transformer
Training Objective	Supervised Fine-Tuning (SFT)

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Training Data

The model was trained on 2,666 airline customer support conversations.

Rather than relying on an existing airline dataset, the training data was created through a multi-stage automated pipeline that transformed general customer support conversations into realistic airline-specific interactions.

The dataset generation process included:

• Data preprocessing
• LLM-based convertibility classification
• Airline domain transformation
• post-processing and cleaning
• JSON validation
• Sample-based manual review

The complete dataset is available on Hugging Face:

Dataset: https://huggingface.co/datasets/Lavanya177/airline-support-tone-clean

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Training Configuration

Parameter	Value
Base Model	Llama 3.2 3B Instruct
Fine-Tuning Method	QLoRA
LoRA Rank	32
LoRA Alpha	64
Learning Rate	1e-3
Epochs	3
Batch Size	8
Gradient Accumulation	4
Optimizer	AdamW
Scheduler	Cosine
Max Sequence Length	2048
Precision	BF16

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Evaluation

Metric	Value
Training Loss	0.4615
Validation Loss	0.5557
Test Loss	0.62

The model demonstrates good convergence while maintaining strong generalization on unseen airline customer support conversations.

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Intended Use

SkyAssist-Llama is intended for:

• Airline customer support assistants
• Conversational AI research
• Educational purposes
• Domain adaptation experiments
• LLM fine-tuning research

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Limitations

This model:

• does not access live airline booking systems
• cannot retrieve reservation details
• cannot process ticket changes or refunds
• may generate incorrect airline-specific policies
• should not replace official airline customer support

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Usage

Load the model

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Lavanya177/SkyAssist-Llama"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto"
)

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Example

System Prompt

You are a professional airline customer support assistant...

User

My baggage has not arrived. What should I do?

Assistant

I'm sorry to hear that your baggage has not arrived. I understand how frustrating this situation can be.

Please provide your baggage claim number, flight number, travel date, and destination airport so I can assist you further. If you have already reported the issue at the airport, please also share your Property Irregularity Report (PIR) reference number.

Once these details are available, the airline can begin tracking your baggage and provide updates on its status.

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Citation

@misc{skyassistllama2026,
  title={SkyAssist-Llama: A Domain-Specific Airline Customer Support Large Language Model},
  author={Lavanya Singh},
  year={2026}
}

License

This project is released under the MIT License.