#### 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