simple gpt model based on entire works of shakespeare

gpt_shakespeare.sync.ipynb

@@ -0,0 +1,428 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "059837a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.nn import functional as F\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)\n",
+    "block_size = 128\n",
+    "batch_size = 32\n",
+    "max_iters = 4000\n",
+    "learning_rate = 3e-4\n",
+    "eval_every = 500\n",
+    "n_embd = 384\n",
+    "n_head = 8\n",
+    "n_layer = 8\n",
+    "dropout = 0.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1fdc69a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"shakespeare.txt\") as f:\n",
+    "    text = f.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0c09eeb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chars = sorted(set(text))\n",
+    "vocab_size = len(chars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a278e7b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vocab size: 101\n",
+      "Text length: 5357910\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Vocab size: {vocab_size}\")\n",
+    "print(f\"Text length: {len(text)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2a540d96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "string_to_int = {ch: i for i, ch in enumerate(chars)}\n",
+    "int_to_string = {i: ch for i, ch in enumerate(chars)}\n",
+    "\n",
+    "encode = lambda s: [string_to_int[ch] for ch in s]\n",
+    "decode = lambda x: ''.join([int_to_string[i] for i in x])\n",
+    "\n",
+    "data = torch.tensor(encode(text), dtype=torch.long, device=device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c7c8e4aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = int(0.8 * len(data))\n",
+    "train_data = data[:n]\n",
+    "val_data = data[n:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "54d80f45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_batch(split):\n",
+    "    data = train_data if split == 'train' else val_data\n",
+    "    ix = torch.randint(len(data) - block_size, (batch_size,))\n",
+    "    x = torch.stack([data[i:i+block_size] for i in ix])\n",
+    "    y = torch.stack([data[i+1:i+block_size+1] for i in ix])\n",
+    "    x, y = x.to(device), y.to(device)\n",
+    "    return x, y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "618df2dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@torch.no_grad()\n",
+    "def estimate_loss():\n",
+    "    out = {}\n",
+    "    model.eval()\n",
+    "    for split in ['train', 'val']:\n",
+    "        losses = torch.zeros(eval_every)\n",
+    "        for k in range(eval_every):\n",
+    "            X, Y = get_batch(split)\n",
+    "            logits, loss = model(X, Y)\n",
+    "            losses[k] = loss.item()\n",
+    "        out[split] = losses.mean()\n",
+    "    model.train()\n",
+    "    return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d0a21928",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step: 0, train loss: 4.615, val loss: 4.613\n",
+      "step: 500, train loss: 1.923, val loss: 1.961\n",
+      "step: 1000, train loss: 1.662, val loss: 1.753\n",
+      "step: 1500, train loss: 1.531, val loss: 1.655\n",
+      "step: 2000, train loss: 1.453, val loss: 1.608\n",
+      "step: 2500, train loss: 1.398, val loss: 1.567\n",
+      "step: 3000, train loss: 1.365, val loss: 1.543\n",
+      "step: 3500, train loss: 1.340, val loss: 1.529\n",
+      "1.3418211936950684\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "class Head(nn.Module):\n",
+    "    \"\"\" one head of self-attention \"\"\"\n",
+    "\n",
+    "    def __init__(self, head_size):\n",
+    "        super().__init__()\n",
+    "        self.key = nn.Linear(n_embd, head_size, bias=False)\n",
+    "        self.query = nn.Linear(n_embd, head_size, bias=False)\n",
+    "        self.value = nn.Linear(n_embd, head_size, bias=False)\n",
+    "        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))\n",
+    "\n",
+    "        self.dropout = nn.Dropout(dropout)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # input of size (batch, time-step, channels)\n",
+    "        # output of size (batch, time-step, head size)\n",
+    "        B,T,C = x.shape\n",
+    "        k = self.key(x)   # (B,T,hs)\n",
+    "        q = self.query(x) # (B,T,hs)\n",
+    "        # compute attention scores (\"affinities\")\n",
+    "        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)\n",
+    "        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)\n",
+    "        wei = F.softmax(wei, dim=-1) # (B, T, T)\n",
+    "        wei = self.dropout(wei)\n",
+    "        # perform the weighted aggregation of the values\n",
+    "        v = self.value(x) # (B,T,hs)\n",
+    "        out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)\n",
+    "        return out\n",
+    "\n",
+    "# [1, 0, 0]\n",
+    "# [1, 0.6, 0]\n",
+    "# [1, 0.6, 0.4]\n",
+    "class MultiHeadAttention(nn.Module):\n",
+    "    \"\"\" multiple heads of self-attention in parallel \"\"\"\n",
+    "\n",
+    "    def __init__(self, num_heads, head_size):\n",
+    "        super().__init__()\n",
+    "        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n",
+    "        self.proj = nn.Linear(head_size * num_heads, n_embd)\n",
+    "        self.dropout = nn.Dropout(dropout)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3])\n",
+    "        out = self.dropout(self.proj(out))\n",
+    "        return out\n",
+    "    \n",
+    "\n",
+    "class FeedFoward(nn.Module):\n",
+    "    \"\"\" a simple linear layer followed by a non-linearity \"\"\"\n",
+    "\n",
+    "    def __init__(self, n_embd):\n",
+    "        super().__init__()\n",
+    "        self.net = nn.Sequential(\n",
+    "            nn.Linear(n_embd, 4 * n_embd),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(4 * n_embd, n_embd),\n",
+    "            nn.Dropout(dropout),\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return self.net(x)\n",
+    "    \n",
+    "class Block(nn.Module):\n",
+    "    \"\"\" Transformer block: communication followed by computation \"\"\"\n",
+    "\n",
+    "    def __init__(self, n_embd, n_head):\n",
+    "        # n_embd: embedding dimension, n_head: the number of heads we'd like\n",
+    "        super().__init__()\n",
+    "        head_size = n_embd // n_head\n",
+    "        self.sa = MultiHeadAttention(n_head, head_size)\n",
+    "        self.ffwd = FeedFoward(n_embd)\n",
+    "        self.ln1 = nn.LayerNorm(n_embd)\n",
+    "        self.ln2 = nn.LayerNorm(n_embd)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        y = self.sa(x)\n",
+    "        x = self.ln1(x + y)\n",
+    "        y = self.ffwd(x)\n",
+    "        x = self.ln2(x + y)\n",
+    "        return x\n",
+    "    \n",
+    "class GPTLanguageModel(nn.Module):\n",
+    "    def __init__(self, vocab_size):\n",
+    "        super().__init__()\n",
+    "        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)\n",
+    "        self.position_embedding_table = nn.Embedding(block_size, n_embd)\n",
+    "        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n",
+    "        self.ln_f = nn.LayerNorm(n_embd) # final layer norm\n",
+    "        self.lm_head = nn.Linear(n_embd, vocab_size)\n",
+    "        \n",
+    "        \n",
+    "        self.apply(self._init_weights)\n",
+    "\n",
+    "    def _init_weights(self, module):\n",
+    "        if isinstance(module, nn.Linear):\n",
+    "            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n",
+    "            if module.bias is not None:\n",
+    "                torch.nn.init.zeros_(module.bias)\n",
+    "        elif isinstance(module, nn.Embedding):\n",
+    "            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n",
+    "\n",
+    "    def forward(self, index, targets=None):\n",
+    "        B, T = index.shape\n",
+    "        \n",
+    "        \n",
+    "        # idx and targets are both (B,T) tensor of integers\n",
+    "        tok_emb = self.token_embedding_table(index) # (B,T,C)\n",
+    "        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)\n",
+    "        x = tok_emb + pos_emb # (B,T,C)\n",
+    "        x = self.blocks(x) # (B,T,C)\n",
+    "        x = self.ln_f(x) # (B,T,C)\n",
+    "        logits = self.lm_head(x) # (B,T,vocab_size)\n",
+    "        \n",
+    "        if targets is None:\n",
+    "            loss = None\n",
+    "        else:\n",
+    "            B, T, C = logits.shape\n",
+    "            logits = logits.view(B*T, C) # reshape to what torch.cross_entropy expects\n",
+    "            targets = targets.view(B*T)\n",
+    "            loss = F.cross_entropy(logits, targets) \n",
+    "        return logits, loss\n",
+    "    \n",
+    "    def generate(self, index, max_new_tokens):\n",
+    "        # index is (B, T) array of indices in the current context\n",
+    "        for _ in range(max_new_tokens):\n",
+    "            # crop idx to the last block_size tokens\n",
+    "            index_cond = index[:, -block_size:]\n",
+    "            # get the predictions\n",
+    "            logits, loss = self.forward(index_cond)\n",
+    "            # focus only on the last time step\n",
+    "            logits = logits[:, -1, :] # becomes (B, C)\n",
+    "            # apply softmax to get probabilities\n",
+    "            probs = F.softmax(logits, dim=-1) # (B, C)\n",
+    "            # sample from the distribution\n",
+    "            index_next = torch.multinomial(probs, num_samples=1) # (B, 1)\n",
+    "            # append sampled index to the running sequence\n",
+    "            index = torch.cat((index, index_next), dim=1) # (B, T+1)\n",
+    "        return index\n",
+    "\n",
+    "model = GPTLanguageModel(vocab_size).to(device)\n",
+    "\n",
+    "# create a PyTorch optimizer\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n",
+    "\n",
+    "for iter in range(max_iters):\n",
+    "    if iter % eval_every == 0:\n",
+    "        losses = estimate_loss()\n",
+    "        print(f\"step: {iter}, train loss: {losses['train']:.3f}, val loss: {losses['val']:.3f}\")\n",
+    "\n",
+    "    # sample a batch of data\n",
+    "    xb, yb = get_batch('train')\n",
+    "\n",
+    "    # evaluate the loss\n",
+    "    logits, loss = model.forward(xb, yb)\n",
+    "    optimizer.zero_grad(set_to_none=True)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "print(loss.item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "99a66247",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\t them part it. The leison drows\n",
+      "Let them napposes them.\n",
+      "\n",
+      "SUFFUE.\n",
+      "Yea, erow now, he was near angless.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "context = torch.zeros((1,1), dtype=torch.long, device=device)\n",
+    "generated_chars = decode(model.generate(context, max_new_tokens=100)[0].tolist())\n",
+    "print(generated_chars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c2b03115",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "To be or not to be, my lord; and at Worces and will.\n",
+      "\n",
+      "FRIAR L JOHN.\n",
+      "My dory Gold Catesby say the King vow you are.\n",
+      "\n",
+      "ENT\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "prompt = 'To be or not to be,'\n",
+    "context = torch.tensor(encode(prompt), dtype=torch.long, device=device)\n",
+    "generated_chars = decode(model.generate(context.unsqueeze(0), max_new_tokens=100)[0].tolist())\n",
+    "print(generated_chars)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

gpt_shakespeare.sync.py

@@ -0,0 +1,251 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.3.4
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+# ---
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(device)
+block_size = 128
+batch_size = 32
+max_iters = 4000
+learning_rate = 3e-4
+eval_every = 500
+n_embd = 384
+n_head = 8
+n_layer = 8
+dropout = 0.2
+
+# %%
+with open("shakespeare.txt") as f:
+    text = f.read()
+# %%
+chars = sorted(set(text))
+vocab_size = len(chars)
+
+# %%
+print(f"Vocab size: {vocab_size}")
+print(f"Text length: {len(text)}")
+
+# %%
+string_to_int = {ch: i for i, ch in enumerate(chars)}
+int_to_string = {i: ch for i, ch in enumerate(chars)}
+
+encode = lambda s: [string_to_int[ch] for ch in s]
+decode = lambda x: ''.join([int_to_string[i] for i in x])
+
+data = torch.tensor(encode(text), dtype=torch.long, device=device)
+
+
+# %%
+n = int(0.8 * len(data))
+train_data = data[:n]
+val_data = data[n:]
+
+# %%
+def get_batch(split):
+    data = train_data if split == 'train' else val_data
+    ix = torch.randint(len(data) - block_size, (batch_size,))
+    x = torch.stack([data[i:i+block_size] for i in ix])
+    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
+    x, y = x.to(device), y.to(device)
+    return x, y
+
+# %%
+@torch.no_grad()
+def estimate_loss():
+    out = {}
+    model.eval()
+    for split in ['train', 'val']:
+        losses = torch.zeros(eval_every)
+        for k in range(eval_every):
+            X, Y = get_batch(split)
+            logits, loss = model(X, Y)
+            losses[k] = loss.item()
+        out[split] = losses.mean()
+    model.train()
+    return out
+
+# %%
+
+class Head(nn.Module):
+    """ one head of self-attention """
+
+    def __init__(self, head_size):
+        super().__init__()
+        self.key = nn.Linear(n_embd, head_size, bias=False)
+        self.query = nn.Linear(n_embd, head_size, bias=False)
+        self.value = nn.Linear(n_embd, head_size, bias=False)
+        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        # input of size (batch, time-step, channels)
+        # output of size (batch, time-step, head size)
+        B,T,C = x.shape
+        k = self.key(x)   # (B,T,hs)
+        q = self.query(x) # (B,T,hs)
+        # compute attention scores ("affinities")
+        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
+        wei = F.softmax(wei, dim=-1) # (B, T, T)
+        wei = self.dropout(wei)
+        # perform the weighted aggregation of the values
+        v = self.value(x) # (B,T,hs)
+        out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)
+        return out
+
+# [1, 0, 0]
+# [1, 0.6, 0]
+# [1, 0.6, 0.4]
+class MultiHeadAttention(nn.Module):
+    """ multiple heads of self-attention in parallel """
+
+    def __init__(self, num_heads, head_size):
+        super().__init__()
+        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
+        self.proj = nn.Linear(head_size * num_heads, n_embd)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1) # (B, T, F) -> (B, T, [h1, h1, h1, h1, h2, h2, h2, h2, h3, h3, h3, h3])
+        out = self.dropout(self.proj(out))
+        return out
+    
+
+class FeedFoward(nn.Module):
+    """ a simple linear layer followed by a non-linearity """
+
+    def __init__(self, n_embd):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(n_embd, 4 * n_embd),
+            nn.ReLU(),
+            nn.Linear(4 * n_embd, n_embd),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+    
+class Block(nn.Module):
+    """ Transformer block: communication followed by computation """
+
+    def __init__(self, n_embd, n_head):
+        # n_embd: embedding dimension, n_head: the number of heads we'd like
+        super().__init__()
+        head_size = n_embd // n_head
+        self.sa = MultiHeadAttention(n_head, head_size)
+        self.ffwd = FeedFoward(n_embd)
+        self.ln1 = nn.LayerNorm(n_embd)
+        self.ln2 = nn.LayerNorm(n_embd)
+
+    def forward(self, x):
+        y = self.sa(x)
+        x = self.ln1(x + y)
+        y = self.ffwd(x)
+        x = self.ln2(x + y)
+        return x
+    
+class GPTLanguageModel(nn.Module):
+    def __init__(self, vocab_size):
+        super().__init__()
+        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
+        self.position_embedding_table = nn.Embedding(block_size, n_embd)
+        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
+        self.ln_f = nn.LayerNorm(n_embd) # final layer norm
+        self.lm_head = nn.Linear(n_embd, vocab_size)
+        
+        
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, index, targets=None):
+        B, T = index.shape
+        
+        
+        # idx and targets are both (B,T) tensor of integers
+        tok_emb = self.token_embedding_table(index) # (B,T,C)
+        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
+        x = tok_emb + pos_emb # (B,T,C)
+        x = self.blocks(x) # (B,T,C)
+        x = self.ln_f(x) # (B,T,C)
+        logits = self.lm_head(x) # (B,T,vocab_size)
+        
+        if targets is None:
+            loss = None
+        else:
+            B, T, C = logits.shape
+            logits = logits.view(B*T, C) # reshape to what torch.cross_entropy expects
+            targets = targets.view(B*T)
+            loss = F.cross_entropy(logits, targets) 
+        return logits, loss
+    
+    def generate(self, index, max_new_tokens):
+        # index is (B, T) array of indices in the current context
+        for _ in range(max_new_tokens):
+            # crop idx to the last block_size tokens
+            index_cond = index[:, -block_size:]
+            # get the predictions
+            logits, loss = self.forward(index_cond)
+            # focus only on the last time step
+            logits = logits[:, -1, :] # becomes (B, C)
+            # apply softmax to get probabilities
+            probs = F.softmax(logits, dim=-1) # (B, C)
+            # sample from the distribution
+            index_next = torch.multinomial(probs, num_samples=1) # (B, 1)
+            # append sampled index to the running sequence
+            index = torch.cat((index, index_next), dim=1) # (B, T+1)
+        return index
+
+model = GPTLanguageModel(vocab_size).to(device)
+
+# create a PyTorch optimizer
+optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
+
+for iter in range(max_iters):
+    if iter % eval_every == 0:
+        losses = estimate_loss()
+        print(f"step: {iter}, train loss: {losses['train']:.3f}, val loss: {losses['val']:.3f}")
+
+    # sample a batch of data
+    xb, yb = get_batch('train')
+
+    # evaluate the loss
+    logits, loss = model.forward(xb, yb)
+    optimizer.zero_grad(set_to_none=True)
+    loss.backward()
+    optimizer.step()
+print(loss.item())
+
+# %%
+
+context = torch.zeros((1,1), dtype=torch.long, device=device)
+generated_chars = decode(model.generate(context, max_new_tokens=100)[0].tolist())
+print(generated_chars)
+
+
+# %%
+
+prompt = 'To be or not to be,'
+context = torch.tensor(encode(prompt), dtype=torch.long, device=device)
+generated_chars = decode(model.generate(context.unsqueeze(0), max_new_tokens=100)[0].tolist())
+print(generated_chars)