diff --git a/gpt.py b/gpt.py
index 81f50c42..fabe3de6 100644
--- a/gpt.py
+++ b/gpt.py
@@ -3,16 +3,16 @@
 from torch.nn import functional as F
 
 # hyperparameters
-batch_size = 64 # how many independent sequences will we process in parallel?
-block_size = 256 # what is the maximum context length for predictions?
+batch_size = 64  # how many independent sequences will we process in parallel?
+block_size = 256  # what is the maximum context length for predictions?
 max_iters = 5000
 eval_interval = 500
 learning_rate = 3e-4
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 eval_iters = 200
-n_embd = 384
-n_head = 6
-n_layer = 6
+n_embds = 384
+n_heads = 6
+n_layers = 6
 dropout = 0.2
 # ------------
 
@@ -26,27 +26,29 @@
 chars = sorted(list(set(text)))
 vocab_size = len(chars)
 # create a mapping from characters to integers
-stoi = { ch:i for i,ch in enumerate(chars) }
-itos = { i:ch for i,ch in enumerate(chars) }
-encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
-decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
+stoi = {ch: i for i, ch in enumerate(chars)}
+itos = {i: ch for i, ch in enumerate(chars)}
+encode = lambda s: [stoi[c] for c in s]  # encoder: take a string, output a list of integers
+decode = lambda l: ''.join([itos[i] for i in l])  # decoder: take a list of integers, output a string
 
 # Train and test splits
 data = torch.tensor(encode(text), dtype=torch.long)
-n = int(0.9*len(data)) # first 90% will be train, rest val
+n = int(0.9 * len(data))  # first 90% will be train, rest val
 train_data = data[:n]
 val_data = data[n:]
 
+
 # data loading
 def get_batch(split):
     # generate a small batch of data of inputs x and targets y
-    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])
+    phase_data = train_data if split == 'train' else val_data
+    ix = torch.randint(len(phase_data) - block_size, (batch_size,))
+    x = torch.stack([phase_data[i:i + block_size] for i in ix])
+    y = torch.stack([phase_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 = {}
@@ -54,17 +56,18 @@ def estimate_loss():
     for split in ['train', 'val']:
         losses = torch.zeros(eval_iters)
         for k in range(eval_iters):
-            X, Y = get_batch(split)
-            logits, loss = model(X, Y)
+            inputs, outputs = get_batch(split)
+            logits, loss = model(inputs, outputs)
             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):
+    def __init__(self, n_embd, head_size):
         super().__init__()
         self.key = nn.Linear(n_embd, head_size, bias=False)
         self.query = nn.Linear(n_embd, head_size, bias=False)
@@ -74,25 +77,26 @@ def __init__(self, head_size):
         self.dropout = nn.Dropout(dropout)
 
     def forward(self, x):
-        B,T,C = x.shape
-        k = self.key(x)   # (B,T,C)
-        q = self.query(x) # (B,T,C)
+        b, t, ch = x.shape
+        k = self.key(x)  # (B,T,C)
+        q = self.query(x)  # (B,T,C)
         # compute attention scores ("affinities")
-        wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, 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 = q @ k.transpose(-2, -1) * ch ** -0.5  # (B, T, C) @ (B, C, 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,C)
-        out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)
+        v = self.value(x)  # (B,T,C)
+        out = wei @ v  # (B, T, T) @ (B, T, C) -> (B, T, C)
         return out
 
+
 class MultiHeadAttention(nn.Module):
     """ multiple heads of self-attention in parallel """
 
-    def __init__(self, num_heads, head_size):
+    def __init__(self, n_embd, num_heads, head_size):
         super().__init__()
-        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
+        self.heads = nn.ModuleList([Head(n_embd, head_size) for _ in range(num_heads)])
         self.proj = nn.Linear(n_embd, n_embd)
         self.dropout = nn.Dropout(dropout)
 
@@ -101,6 +105,7 @@ def forward(self, x):
         out = self.dropout(self.proj(out))
         return out
 
+
 class FeedFoward(nn.Module):
     """ a simple linear layer followed by a non-linearity """
 
@@ -116,6 +121,7 @@ def __init__(self, n_embd):
     def forward(self, x):
         return self.net(x)
 
+
 class Block(nn.Module):
     """ Transformer block: communication followed by computation """
 
@@ -123,7 +129,7 @@ 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.sa = MultiHeadAttention(n_embd, n_head, head_size)
         self.ffwd = FeedFoward(n_embd)
         self.ln1 = nn.LayerNorm(n_embd)
         self.ln2 = nn.LayerNorm(n_embd)
@@ -133,35 +139,36 @@ def forward(self, x):
         x = x + self.ffwd(self.ln2(x))
         return x
 
+
 # super simple bigram model
 class BigramLanguageModel(nn.Module):
 
-    def __init__(self):
+    def __init__(self, n_embd, n_head, n_layer):
         super().__init__()
         # each token directly reads off the logits for the next token from a lookup table
         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.ln_f = nn.LayerNorm(n_embd)  # final layer norm
         self.lm_head = nn.Linear(n_embd, vocab_size)
 
     def forward(self, idx, targets=None):
-        B, T = idx.shape
+        b, t = idx.shape
 
         # idx and targets are both (B,T) tensor of integers
-        tok_emb = self.token_embedding_table(idx) # (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)
+        tok_emb = self.token_embedding_table(idx)  # (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)
-            targets = targets.view(B*T)
+            b, t, ch = logits.shape
+            logits = logits.view(b * t, ch)
+            targets = targets.view(b * t)
             loss = F.cross_entropy(logits, targets)
 
         return logits, loss
@@ -174,40 +181,40 @@ def generate(self, idx, max_new_tokens):
             # get the predictions
             logits, loss = self(idx_cond)
             # focus only on the last time step
-            logits = logits[:, -1, :] # becomes (B, C)
+            logits = logits[:, -1, :]  # becomes (B, C)
             # apply softmax to get probabilities
-            probs = F.softmax(logits, dim=-1) # (B, C)
+            probs = F.softmax(logits, dim=-1)  # (B, C)
             # sample from the distribution
-            idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
+            idx_next = torch.multinomial(probs, num_samples=1)  # (B, 1)
             # append sampled index to the running sequence
-            idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
+            idx = torch.cat((idx, idx_next), dim=1)  # (B, T+1)
         return idx
 
-model = BigramLanguageModel()
+
+model = BigramLanguageModel(n_embds, n_heads, n_layers)
 m = model.to(device)
 # print the number of parameters in the model
-print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')
+print(sum(p.numel() for p in m.parameters()) / 1e6, 'M parameters')
 
 # create a PyTorch optimizer
 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
 
-for iter in range(max_iters):
-
+for i_iter in range(max_iters):
     # every once in a while evaluate the loss on train and val sets
-    if iter % eval_interval == 0 or iter == max_iters - 1:
-        losses = estimate_loss()
-        print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
+    if i_iter % eval_interval == 0 or i_iter == max_iters - 1:
+        estimated_losses = estimate_loss()
+        print(f"step {i_iter}: train loss {estimated_losses['train']:.4f}, val loss {estimated_losses['val']:.4f}")
 
     # sample a batch of data
     xb, yb = get_batch('train')
 
     # evaluate the loss
-    logits, loss = model(xb, yb)
+    output_logits, output_loss = model(xb, yb)
     optimizer.zero_grad(set_to_none=True)
-    loss.backward()
+    output_loss.backward()
     optimizer.step()
 
 # generate from the model
 context = torch.zeros((1, 1), dtype=torch.long, device=device)
 print(decode(m.generate(context, max_new_tokens=500)[0].tolist()))
-#open('more.txt', 'w').write(decode(m.generate(context, max_new_tokens=10000)[0].tolist()))
+# open('more.txt', 'w').write(decode(m.generate(context, max_new_tokens=10000)[0].tolist()))