chore: Restructure

models/__init__.py

@@ -1 +0,0 @@
-from .cnn import CNNPredictor

models/cnn/__init__.py

@@ -1 +0,0 @@
-from .cnn import CNNPredictor

models/cnn/cnn.py

@@ -1,52 +0,0 @@
-import torch
-import torch.nn as nn
-
-class CNNPredictor(nn.Module):
-    def __init__(
-            self, 
-            vocab_size=256, 
-            embed_dim=64, 
-            hidden_dim=128,
-        ):
-        super().__init__()
-
-        # 1. Embedding: maps bytes (0–255) → vectors
-        self.embed = nn.Embedding(vocab_size, embed_dim)
-
-        # 2. Convolutional feature extractor
-        self.conv_layers = nn.Sequential(
-            nn.Conv1d(embed_dim, hidden_dim, kernel_size=5, padding=2),
-            nn.ReLU(),
-            nn.Conv1d(hidden_dim, hidden_dim, kernel_size=5, padding=2),
-            nn.ReLU(),
-            nn.Conv1d(hidden_dim, hidden_dim, kernel_size=5, padding=2),
-            nn.ReLU(),
-        )
-
-        # 3. Global pooling to collapse sequence length
-        self.pool = nn.AdaptiveAvgPool1d(1)  # → (B, hidden_channels, 1)
-
-        # 4. Final classifier
-        self.fc = nn.Linear(hidden_dim, vocab_size)  # → (B, 256)
-
-    def forward(self, x):
-        """
-        x: LongTensor of shape (B, 128), values 0-255
-        """
-        # embed: (B, 128, embed_dim)
-        x = self.embed(x)
-
-        # conv1d expects (B, C_in, L) → swap dims
-        x = x.transpose(1, 2)  # (B, embed_dim, 128)
-
-        # apply CNN
-        x = self.conv_layers(x)  # (B, hidden_channels, 128)
-
-        # global average pooling over sequence
-        x = self.pool(x).squeeze(-1)  # (B, hidden_channels)
-
-        # final classifier
-        logits = self.fc(x)  # (B, 256)
-        return logits
-
-