RWKV docs

labmlai · Mar 17, 2024 · df9e1af · df9e1af
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diff --git a/docs/index.html b/docs/index.html
@@ -124,9 +124,7 @@ <h4>✨ <a href="resnet/index.html">ResNet</a></h4>
 <h4>✨ <a href="conv_mixer/index.html">ConvMixer</a></h4>
 <h4>✨ <a href="capsule_networks/index.html">Capsule Networks</a></h4>
 <h4>✨ <a href="unet/index.html">U-Net</a></h4>
-<h4>✨ <a href="sketch_rnn/index.html">RNNs</a></h4>
-<ul><li><a href="rwkv/index.html">RWKV</a> </li>
-<li><a href="sketch_rnn/index.html">Sketch RNN</a></li></ul>
+<h4>✨ <a href="sketch_rnn/index.html">Sketch RNN</a></h4>
 <h4>✨ Graph Neural Networks</h4>
 <ul><li><a href="graphs/gat/index.html">Graph Attention Networks (GAT)</a> </li>
 <li><a href="graphs/gatv2/index.html">Graph Attention Networks v2 (GATv2)</a></li></ul>
@@ -170,7 +168,6 @@ <h2>Highlighted Research Paper PDFs</h2>
 <ul><li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.10628.pdf">Autoregressive Search Engines: Generating Substrings as Document Identifiers</a> </li>
 <li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.15556.pdf">Training Compute-Optimal Large Language Models</a> </li>
 <li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1910.02054.pdf">ZeRO: Memory Optimizations Toward Training Trillion Parameter Models</a> </li>
-<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/RWKV.pdf">RWKV: Reinventing RNNs for the Transformer Era</a> </li>
 <li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.02311.pdf">PaLM: Scaling Language Modeling with Pathways</a> </li>
 <li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/dall-e-2.pdf">Hierarchical Text-Conditional Image Generation with CLIP Latents</a> </li>
 <li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.14465.pdf">STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning</a> </li>

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