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1. Step 1: Choose a Model Architecture and Framework

• Architecture:

1. Simple RNN/GRU: TensorFlow/Keras or PyTorch
2. Single-headed Transformer Encoder: TensorFlow/Keras or Hugging Face Transformers

• Resources:

1. TensorFlow Tutorials: https://www.tensorflow.org/tutorials
2. PyTorch Tutorials: https://pytorch.org/tutorials
3. 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.