Llama from scratch (or how to implement a paper without crying)
a year ago
- #transformer models
- #machine learning
- #Llama implementation
- The article provides a guide on implementing a scaled-down version of the Llama model for training on TinyShakespeare, inspired by Karpathy's Makemore series.
- Key takeaways include working iteratively, starting small, and building up, with emphasis on testing layers and ensuring they function as expected.
- The implementation covers three architectural modifications from the original Transformer: RMSNorm for pre-normalization, Rotary embeddings, and SwiGLU activation function.
- Detailed steps include setting up the dataset, creating helper functions for model evaluation, and iteratively adding model components like attention mechanisms and normalization layers.
- The article highlights the importance of debugging, such as checking gradient flows and experimenting with hyperparameters to optimize model performance.
- Final model performance is evaluated on a test set, showing a loss metric, and the article concludes with lessons on starting simple and the importance of iterative development.