diff --git a/jit-train-makemore.py b/jit-train-makemore.py
new file mode 100644
index 0000000..721ab12
--- /dev/null
+++ b/jit-train-makemore.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+
+from tinygrad.tensor import Tensor
+from tinygrad.helpers import dtypes
+from tinygrad.nn import Linear, Embedding
+from tinygrad.nn.optim import SGD
+from tinygrad.jit import TinyJit
+from extra.training import sparse_categorical_crossentropy
+import random
+import numpy as np
+import numpy.random as npr
+
+words = ['lorem', 'ipsum', 'dolor', 'sit', 'amet', 'consectetur', 'adipiscing',
+         'elit', 'sed', 'do', 'eiusmod', 'tempor', 'incididunt', 'ut', 'labore', 'et',
+         'dolore', 'magna', 'aliqua', 'ut', 'enim', 'ad', 'minim', 'veniam', 'quis',
+         'nostrud', 'exercitation', 'ullamco', 'laboris', 'nisi', 'ut', 'aliquip', 'ex',
+         'ea', 'commodo', 'consequat', 'duis', 'aute', 'irure', 'dolor', 'in',
+         'reprehenderit', 'in', 'voluptate', 'velit', 'esse', 'cillum', 'dolore', 'eu',
+         'fugiat', 'nulla', 'pariatur', 'excepteur', 'sint', 'occaecat', 'cupidatat',
+         'non', 'proident', 'sunt', 'in', 'culpa', 'qui', 'officia', 'deserunt',
+         'mollit', 'anim', 'id', 'est', 'laborum']
+
+def atoi(char):
+    if char == '.':
+        return 0
+    assert char >= "a" and char <= "z"
+    return ord(char) - ord("a") + 1
+
+def itoa(char):
+    charint = int(char)
+    if charint == 0:
+        return "."
+    assert char >= 1 and char <= 26
+    return chr(charint -1 + ord('a'))
+
+def dataset(words, block_size=3):
+    X, Y = [], []
+    for word in words:
+        window = [0] * block_size  # sliding window context
+        for ch in word + ".":
+            ix = atoi(ch)
+            X.append(window)
+            Y.append(ix)
+            window = window[1:] + [ix]
+    return np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32)
+
+class Model:
+    def __init__(self, emb_size=10, hidden_n=100, vocab_size=27):
+        self.emb = Embedding(vocab_size, emb_size)
+        self.l1 = Linear(emb_size * block_size, hidden_n)
+        self.l2 = Linear(hidden_n, vocab_size)
+
+    def __call__(self, x, training=True):
+        if training:
+            for p in self.parameters():
+                p.requires_grad = True
+        emb = self.emb(x)
+        h = self.l1(emb.reshape(emb.shape[0], -1)).tanh()
+        logits = self.l2(h)
+        return logits
+
+    def parameters(self):
+        return [self.l1.weight, self.l1.bias, self.l2.weight, self.l2.bias, self.emb.weight]
+
+@TinyJit
+def jitloss(x,y):
+  return sparse_categorical_crossentropy(x, y).realize()
+
+block_size=3
+random.shuffle(words)
+X,Y = dataset(words, block_size)
+m = Model()
+opt = SGD(m.parameters(), lr=0.1)
+
+for step in range(1000):
+    batch_ix = npr.randint(0, X.shape[0], (32,))
+    x_batch, y_batch = Tensor(X[batch_ix], requires_grad=False), Y[batch_ix]
+
+    logits = m(x_batch)
+    loss = jitloss(logits, y_batch)
+    print(loss.grad.numpy())
+    opt.zero_grad()
+
+    loss.backward()
+    if m.parameters()[0].grad is None:
+        break
+
+    opt.step()
+
+    if step % 100 == 0:
+        print(f"Step {step+1} | Loss: {loss.numpy()}")