1.9 KiB
1.9 KiB
1. Implement Model Checkpointing and Serialization
Implement serialization for model parameters
Save/load optimizer state for resuming training
Add versioning to handle model format changes
2. Add Validation and Evaluation Pipeline
Implement a validation dataset split
Add evaluation metrics (perplexity, accuracy, etc.)
Create a proper test harness for benchmarking
3. Improve the Training Loop
Add learning rate scheduling
Implement gradient clipping
Add early stopping based on validation performance
Create training progress visualization
4. Enhance the Tokenizer
Add support for special tokens (UNK, PAD, BOS, EOS)
Implement vocabulary trimming/pruning
Add serialization/deserialization for the tokenizer
5. Implement Text Generation
Add inference methods for text generation
Implement sampling strategies (greedy, beam search, temperature)
Create a demo script to showcase model capabilities
6. Optimize Performance
Add CUDA support if not already implemented
Implement mixed-precision training
Optimize data loading and preprocessing pipeline
7. Create Examples and Documentation
Build example scripts for common use cases
Create comprehensive documentation
Add unit tests for critical components
8. Extend Model Architectures
Implement different attention mechanisms
Add support for different model sizes (small, medium, large)
Experiment with architectural variations
9. Add Dataset Support
Implement support for common NLP datasets
Create data preprocessing pipelines
Add data augmentation techniques
10. Build a Simple Interface/API
Create a simple Python API for training and inference
Add command-line interface for common operations
Consider building a simple web demo