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:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
  1. Configure your Hugging Face token:
huggingface-cli login

Project Structure

.
β”œβ”€β”€ config/
β”‚   └── config.json         # Training configuration
β”œβ”€β”€ data/
β”‚   └── 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:

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:

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:

huggingface-cli upload rahmanazhar/Travereel-Model-V1 output/

License

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

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