Add PyPI package (#576)

* Add PyPI package

* fixes

* fixes

pkg/llms_from_scratch/tests/test_appendix_a.py

@@ -0,0 +1,70 @@
+# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
+# Source for "Build a Large Language Model From Scratch"
+#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
+# Code: https://github.com/rasbt/LLMs-from-scratch
+
+from llms_from_scratch.appendix_a import NeuralNetwork, ToyDataset
+
+import torch
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+
+
+def test_dataset():
+
+    X_train = torch.tensor([
+        [-1.2, 3.1],
+        [-0.9, 2.9],
+        [-0.5, 2.6],
+        [2.3, -1.1],
+        [2.7, -1.5]
+    ])
+
+    y_train = torch.tensor([0, 0, 0, 1, 1])
+    train_ds = ToyDataset(X_train, y_train)
+
+    len(train_ds) == 5
+    torch.manual_seed(123)
+
+    train_loader = DataLoader(
+        dataset=train_ds,
+        batch_size=2,
+        shuffle=True,
+        num_workers=0
+    )
+
+    torch.manual_seed(123)
+    model = NeuralNetwork(num_inputs=2, num_outputs=2)
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.5)
+
+    num_epochs = 3
+
+    for epoch in range(num_epochs):
+
+        model.train()
+        for batch_idx, (features, labels) in enumerate(train_loader):
+
+            logits = model(features)
+
+            loss = F.cross_entropy(logits, labels)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            print(f"Epoch: {epoch+1:03d}/{num_epochs:03d}"
+                  f" | Batch {batch_idx:03d}/{len(train_loader):03d}"
+                  f" | Train/Val Loss: {loss:.2f}")
+
+        model.eval()
+        with torch.no_grad():
+            outputs = model(X_train)
+
+        expected = torch.tensor([
+            [2.8569, -4.1618],
+            [2.5382, -3.7548],
+            [2.0944, -3.1820],
+            [-1.4814, 1.4816],
+            [-1.7176, 1.7342]
+        ])
+        torch.equal(outputs, expected)