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Step 1: Choose a Model Architecture and Framework

• Architecture:
  • Simple RNN/GRU: TensorFlow/Keras or PyTorch
  • Single-headed Transformer Encoder: TensorFlow/Keras or Hugging Face Transformers
• Resources:
  • TensorFlow Tutorials: https://www.tensorflow.org/tutorials
  • PyTorch Tutorials: https://pytorch.org/tutorials
  • Hugging Face Transformers: https://huggingface.co/transformers/

Step 2: Prepare Your Training Dataset

• Dataset Size: Start with a small, manageable corpus (e.g., BookCorpus, Twitter Sentiment, or domain-specific datasets).
• Preprocessing:
  • Tokenization: NLTK or spaCy
  • Cleaning: pandas or NumPy
  • Formatting: TensorFlow/Keras or PyTorch data loading utilities
• Resources:
  • NLTK: https://www.nltk.org/
  • spaCy: https://spacy.io/
  • pandas: https://pandas.pydata.org/
  • NumPy: https://numpy.org/

Step 3: Implement Model and Training Loop

• Framework: TensorFlow/Keras or PyTorch
• Code Structure:
  • Define model architecture with chosen framework
  • Implement loss function (e.g., cross-entropy)
  • Choose optimizer (e.g., Adam)
  • Set up mini-batch training loop
• Resources:
  • TensorFlow/Keras guides: https://www.tensorflow.org/guide
  • PyTorch tutorials: https://pytorch.org/tutorials

Step 4: Fine-tune and Evaluate

• Training:
  • Monitor loss and adjust hyperparameters
  • Experiment with different learning rates and batch sizes
• Evaluation:
  • Design test tasks for your LLM's functionality
  • Track performance metrics (e.g., accuracy, perplexity)

Step 5: Iterate and Improve

• Experimentation:
  • Try different model architectures or hyperparameters
  • Explore diverse training data or techniques
• Interpretability:
  • Understand model behavior using techniques like attention visualization
  • Address potential biases and limitations
• Resources:
  • JAX: https://github.com/google/jax (for advanced model optimization)
  • TensorBoard: https://www.tensorflow.org/tensorboard (for visualization)

Additional Tips:

• Utilize cloud platforms (Google Colab, Paperspace) for GPU/TPU access if needed.
• Consult open-source LLM projects for inspiration and code examples.
• Engage in online communities and forums for support and knowledge sharing.