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
- Python 3.8 or higher
- CUDA-capable GPU (8GB+ VRAM recommended)
- Hugging Face account and token
Setup
- Create a virtual environment:
python -m venv venv
source 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
.
βββ config/
β βββ config.json # Training configuration
βββ data/
β βββ itineraries.json # Training data
βββ src/
βββ 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:
huggingface-cli upload rahmanazhar/Travereel-Model-V1 output/
License
This project uses FLAN-T5 which is licensed under the Apache 2.0 License.