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
Python 3.8 or higher
CUDA-capable GPU (8GB+ VRAM recommended)
Hugging Face account and token
Setup
Create a virtual environment:
bash
1python -m venv venv
2source venv/bin/activate # On Windows: venv\Scripts\activate
Install dependencies:
pip install -r requirements.txt
Configure your Hugging Face token:
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
Review and adjust the configuration in config/config.json if needed.
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:
