---
language: en
tags:
- flan-t5
- travel
- itinerary-generation
- fine-tuning
license: apache-2.0
datasets:
- custom
model-index:
- name: Travereel-Model-V1
  results: []
---

# T5 Itinerary Generator

A custom fine-tuned version of FLAN-T5 for generating detailed travel itineraries.

## Model Description

This model is fine-tuned from Google's FLAN-T5 to specialize in generating detailed travel itineraries based on user preferences, destinations, duration, and budget constraints.

### Intended Use

- Generate detailed day-by-day travel itineraries
- Provide activity suggestions based on preferences
- Consider budget constraints in planning
- Include practical travel details

### Training Data

The model is trained on a curated dataset of travel itineraries, including:
- Various destinations worldwide
- Different trip durations
- Various travel preferences and styles
- Different budget ranges

## Prerequisites

1. Python 3.8 or higher
2. CUDA-capable GPU (8GB+ VRAM recommended)
3. Hugging Face account and token

## Setup

1. Create a virtual environment:
```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

2. Install dependencies:
```bash
pip install -r requirements.txt
```

3. Configure your Hugging Face token:
```bash
huggingface-cli login
```

## Project Structure

```
.
├── config/
│   └── config.json         # Training configuration
├── data/
│   └── itineraries.json    # Training data
└── src/
    └── train.py           # Training script
```

## Training Data

The training data in `data/itineraries.json` contains examples of travel itineraries with the following structure:
- Destination
- Duration
- Preferences
- Budget
- Detailed day-by-day itinerary

## Training the Model

1. Review and adjust the configuration in `config/config.json` if needed.

2. Start training:
```bash
python src/train.py
```

The script will:
- Load the LLaMA-2 base model
- Fine-tune it on the itinerary dataset
- Save checkpoints during training
- Export the final model

## Model Details

This model is fine-tuned to generate travel itineraries based on:
- Destination
- Duration of stay
- Travel preferences
- Budget constraints

The model learns to:
- Structure day-by-day itineraries
- Balance activities based on preferences
- Consider budget constraints
- Include practical details like transportation and check-in/out

## Output Format

The model generates itineraries in a structured format:
```
Day 1:
- Activity 1
- Activity 2
...

Day 2:
- Activity 1
- Activity 2
...
```

## Monitoring Training

Training progress can be monitored using TensorBoard:
```bash
tensorboard --logdir output/runs
```

## Model Deployment

After training, the model will be saved in the `output` directory. You can upload it to Hugging Face Hub using:
```bash
huggingface-cli upload rahmanazhar/Travereel-Model-V1 output/
```

## License

This project uses FLAN-T5 which is licensed under the Apache 2.0 License.