diff --git a/model.py b/model.py
index 78bacd4..80c2192 100644
--- a/model.py
+++ b/model.py
@@ -25,7 +25,7 @@ def new_gelu(x):
 class LayerNorm(nn.Module):
     """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
 
-    def __init__(self, ndim, bias=True):
+    def __init__(self, ndim, bias):
         super().__init__()
         self.weight = nn.Parameter(torch.ones(ndim))
         self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
@@ -39,9 +39,9 @@ class CausalSelfAttention(nn.Module):
         super().__init__()
         assert config.n_embd % config.n_head == 0
         # key, query, value projections for all heads, but in a batch
-        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=False)
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
         # output projection
-        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
         # regularization
         self.attn_dropout = nn.Dropout(config.dropout)
         self.resid_dropout = nn.Dropout(config.dropout)
@@ -76,8 +76,8 @@ class MLP(nn.Module):
 
     def __init__(self, config):
         super().__init__()
-        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd, bias=False)
-        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd, bias=False)
+        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
         self.dropout = nn.Dropout(config.dropout)
 
     def forward(self, x):
@@ -91,9 +91,9 @@ class Block(nn.Module):
 
     def __init__(self, config):
         super().__init__()
-        self.ln_1 = LayerNorm(config.n_embd, bias=False)
+        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
         self.attn = CausalSelfAttention(config)
-        self.ln_2 = LayerNorm(config.n_embd, bias=False)
+        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
         self.mlp = MLP(config)
 
     def forward(self, x):
@@ -109,6 +109,7 @@ class GPTConfig:
     n_head: int = 12
     n_embd: int = 768
     dropout: float = 0.1
+    bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
 
 class GPT(nn.Module):
 
@@ -123,7 +124,7 @@ class GPT(nn.Module):
             wpe = nn.Embedding(config.block_size, config.n_embd),
             drop = nn.Dropout(config.dropout),
             h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
-            ln_f = LayerNorm(config.n_embd, bias=False),
+            ln_f = LayerNorm(config.n_embd, bias=config.bias),
         ))
         self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
         # with weight tying when using torch.compile() some warnings get generated: