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+# MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers
+
+## Overview
+MEGABYTE introduces a revolutionary approach to handling extremely long sequences by implementing a hierarchical transformer architecture that can process sequences of up to 1 million bytes while maintaining linear complexity. The model achieves this through a novel multi-scale approach that combines local attention patterns with global summary tokens, effectively creating a hierarchical representation of the input sequence. This breakthrough is particularly significant for A2A communications as it enables agents to process and reason about massive amounts of multimodal data without the typical computational bottlenecks associated with traditional transformer architectures.
+
+## Key Features
+- Linear complexity O(n) sequence processing
+- Multi-scale transformer architecture
+- Hierarchical representation learning
+- Efficient handling of long-range dependencies
+- Supports sequences up to 1M bytes
+
+## Technical Implementation
+```python
+class MEGABYTE(nn.Module):
+    def __init__(self, 
+                 vocab_size,
+                 d_model=512,
+                 n_levels=3,
+                 n_heads=8):
+        super().__init__()
+        self.embedding = nn.Embedding(vocab_size, d_model)
+        self.levels = nn.ModuleList([
+            TransformerLevel(d_model, n_heads)
+            for _ in range(n_levels)
+        ])
+        
+    def forward(self, x):
+        # Hierarchical processing
+        x = self.embedding(x)
+        summary_tokens = []
+        for level in self.levels:
+            x, summary = level(x)
+            summary_tokens.append(summary)
+        return x, summary_tokens