AthenAI is a fine-tuned FLAN-T5-base model designed to generate personalized workout routines based on user context, training goals, motivation, and special health situations. The model creates structured workout plans with multiple exercise blocks, duration estimates, and detailed instructions in JSON format.
Model Details
Model Description
AthenAI takes user context as input (training phase, motivation, special situations) and generates comprehensive workout plans tailored to individual needs. The model was trained on synthetic workout data derived from comprehensive exercise databases and can handle various fitness scenarios from weight loss to injury recovery.
Developed by: a-albiol
Model type: Text-to-Text Generation (Sequence-to-Sequence)
Language(s) (NLP): English (primary), Spanish (secondary)
AthenAI is designed for direct use in generating personalized workout routines. Users can input their training context and receive structured workout plans immediately. The model is particularly useful for:
Personal Fitness Applications: Generate daily workout routines
Fitness Apps: Provide adaptive exercise recommendations
Phase 1: Fine-tuning on Kaggle-derived synthetic data (1000 examples)
Phase 2: Additional training on GitHub-derived synthetic data (1000 examples)
Speeds, Sizes, Times
Model Size: ~850MB (FLAN-T5-base architecture)
Training Time: ~2-4 hours per phase on Google Colab GPU
Inference Speed: 0.5-1 seconds per workout (GPU), 2-5 seconds (CPU)
Memory Requirements: 4-8GB RAM for inference, 12GB+ for training
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model was evaluated on held-out synthetic workout data representing diverse user contexts and exercise combinations. Test cases included edge cases such as multiple special situations and complex user requirements.
Factors
Evaluation was performed across multiple factors:
Training Phase Diversity: All four training phases (weight_loss, muscle_gain, cardio_improve, maintenance)
Motivation Variety: Five motivation types from medical to competitive
Special Situations: Seven different special health/physical situations
Workout Complexity: Varying block structures and exercise counts
Metrics
Primary evaluation metrics included:
JSON Format Validity: Structural correctness of generated workout plans
Context Relevance: Appropriateness of exercises for given user context
Exercise Variety: Diversity in recommended exercises across similar contexts
Block Structure Coherence: Logical flow from warmup to cooldown
Duration Estimation Accuracy: Realistic time estimates for workout completion
Results
The model successfully generates valid JSON workout structures with contextually appropriate exercises. Manual evaluation showed strong performance in:
Context understanding and exercise selection
Workout structure and flow
Adaptation to special situations
Exercise variety and avoiding repetition
Environmental Impact
Training AthenAI involved fine-tuning a pre-existing model rather than training from scratch, significantly reducing computational requirements and carbon footprint.
Hardware Type: NVIDIA T4 GPU (Google Colab)
Hours used: Approximately 6-8 hours total training time
Cloud Provider: Google Cloud Platform (Colab)
Compute Region: Variable (Colab allocation)
Carbon Emitted: Estimated <5kg CO2eq (due to fine-tuning approach)
Technical Specifications
Model Architecture and Objective
Architecture: FLAN-T5-base (Text-to-Text Transfer Transformer)
Parameters: ~850 million parameters
Objective: Sequence-to-sequence generation for workout plan creation
Input Format: Natural language context description
Output Format: Structured JSON workout plans
Context Window: 512 tokens maximum
Generation Strategy: Autoregressive text generation with temperature sampling
Compute Infrastructure
Hardware
Training: NVIDIA T4 GPU (Google Colab Pro)
Memory: 16GB GPU memory, 25GB system RAM
Storage: 100GB+ for datasets and model checkpoints
Software
Framework: Hugging Face Transformers 4.45.2
Training Library: Hugging Face Trainer
Data Processing: Pandas, Datasets 3.0.1
Environment: Python 3.10, PyTorch 2.0+
Platform: Google Colab with GPU acceleration
Citation
If you use AthenAI in your research or applications, please cite:
Training Phase: User's current fitness goal (weight loss, muscle gain, etc.)
Motivation: Underlying reason for exercising (medical, competition, etc.)
Special Situation: Health or physical considerations (pregnancy, injury, etc.)
Workout Block: Structured section of workout (warmup, main, cooldown)
Fine-tuning: Process of adapting pre-trained model to specific task
Synthetic Data: Artificially generated training examples based on real exercise databases
More Information
For detailed implementation, training notebooks, and additional examples, visit the project repository. The model continues to evolve with community feedback and additional training data.
For technical support or questions about integration, please open an issue in the repository or contact through Hugging Face model discussions.
Model Card Authors
Primary Author: a-albiol
Contributors: Community feedback and testing
Model Card Contact
For questions, feedback, or collaboration inquiries:
