Created NORA. She has been designed from zero. At this point, I have determined the best hyperparamers for her to train. Next step is to help her communicate on discord and see how she handles it.

data_loader.py

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+"""
+data_loader.py
+
+Loads all .txt files from data_dir, concatenates them, tokenizes them,
+and creates a Dataset of (input_seq, target_seq) for language modeling.
+"""
+
+import os
+import torch
+from torch.utils.data import Dataset, DataLoader
+
+
+class TextDataset(Dataset):
+    def __init__(self, data_dir: str, tokenizer, seq_length: int):
+        """
+        - data_dir: folder of .txt public-domain books.
+        - tokenizer: instance of CharTokenizer (from tokenizer.py).
+        - seq_length: context length in tokens.
+        """
+        super().__init__()
+        self.seq_length = seq_length
+        self.tokenizer = tokenizer
+
+        # Read and concatenate all text files into one long string
+        texts = []
+        for root, _, files in os.walk(data_dir):
+            for fname in files:
+                if not fname.lower().endswith(".txt"):
+                    continue
+                path = os.path.join(root, fname)
+                with open(path, "r", encoding="utf-8", errors="ignore") as f:
+                    texts.append(f.read())
+        full_text = "\n".join(texts)
+        token_ids = self.tokenizer.encode(full_text)
+
+        # Prepare input-target pairs
+        self.examples = []
+        stride = 32
+        for i in range(0, len(token_ids) - seq_length, stride):
+            inp = token_ids[i : i + seq_length]
+            targ = token_ids[i + 1 : i + seq_length + 1]
+            self.examples.append((inp, targ))
+
+    def __len__(self):
+        return len(self.examples)
+
+    def __getitem__(self, idx):
+        inp, targ = self.examples[idx]
+        return torch.tensor(inp, dtype=torch.long), torch.tensor(targ, dtype=torch.long)
+
+
+def get_dataloader(
+    data_dir: str, tokenizer, seq_length: int, batch_size: int, shuffle: bool = True
+) -> DataLoader:
+    dataset = TextDataset(data_dir, tokenizer, seq_length)
+    return DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        drop_last=True,
+        num_workers=8,
+        pin_memory=True,
+    )