The author explains the intuition behind causal scaled dot product self-attention and how to create an expert module. They also discuss the concept of top-k gating and how to create a sparse mixture of experts module. The blog post concludes with a demonstration of how to put all these elements together to create a sparse mixture of experts language model. The author also provides suggestions for potential improvements and modifications to the model.
Key takeaways:
- The blog post provides a detailed walkthrough of implementing a sparse mixture of experts language model from scratch, inspired by Andrej Karpathy's project 'makemore'.
- The sparse mixture of experts model architecture is explained in depth, with a focus on the key elements of this architecture and how they are implemented.
- The author provides code snippets for each part of the implementation, including the creation of an expert module, top-k gating, and the creation of a sparse Mixture of Experts module.
- The post also suggests potential improvements and modifications to the implementation, such as making the Mixture of Experts module more efficient, trying different neural net initialization strategies, and moving from character level to sub-word tokenization.