cross referencing other transformer-related implementations

README.md

@@ -11,10 +11,12 @@ language: en
 license: mit
 ---
 
-# DeepSeek Multi-Latent Attention
+# DeepSeek Multi-Head Latent Attention
 
 This repository provides a PyTorch implementation of the Multi-Head Latent Attention (MLA) mechanism introduced in the DeepSeek-V2 paper. **This is not a trained model, but rather a modular attention implementation** that significantly reduces KV cache for efficient inference while maintaining model performance through its innovative architecture. It can be used as a drop-in attention module in transformer architectures.
 
+This repository is part of a series implementing the key architectural innovations from the DeepSeek paper. See the **Related Implementations** section for the complete series.
+
 ## Key Features
 
 - **Low-Rank Key-Value Joint Compression**: Reduces memory footprint during inference
@@ -114,6 +116,18 @@ Key aspects:
 - Position encoding through decoupled RoPE pathway
 - Efficient cache management for both pathways
 
+## Related Implementations
+
+This repository is part of a series implementing the key architectural innovations from the DeepSeek paper:
+
+1. **[DeepSeek Multi-head Latent Attention](https://huggingface.co/bird-of-paradise/deepseek-mla)**(This Repository): Implementation of DeepSeek's MLA mechanism for efficient KV cache usage during inference.
+
+2. **[DeepSeek MoE](https://huggingface.co/bird-of-paradise/deepseek-moe)**: Implementation of DeepSeek's Mixture of Experts architecture that enables efficient scaling of model parameters.
+
+3. **[Transformer Implementation Tutorial](https://huggingface.co/datasets/bird-of-paradise/transformer-from-scratch-tutorial)**: A detailed tutorial on implementing transformer architecture with explanations of key components.
+
+Together, these implementations cover the core innovations that power DeepSeek's state-of-the-art performance. By combining the MoE architecture with Multi-head Latent Attention, you can build a complete DeepSeek-style model with improved training efficiency and inference performance.
+
 ## Contributing
 
 Contributions are welcome! Feel free to: