diff --git a/model.py b/model.py
index bfbb11d303..887ca56ab4 100644
--- a/model.py
+++ b/model.py
@@ -149,6 +149,91 @@ def forward(self, inputs):
 
         return numerator / denominator
 
+class RotaryEmbedding(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.dim = config.n_embd
+
+        # Register frequencies directly as buffers
+        self.register_buffer('freq_left', (10000 ** (torch.arange(0, self.dim//2).float() / self.dim//2)))
+        self.register_buffer('freq_right',(10000 ** (torch.arange(0, self.dim//2).float() / self.dim//2)))
+
+    def forward(self, x):
+        seq_len = x.shape[-2]
+        device = x.device
+
+        t = torch.arange(seq_len, device=device)
+
+        # Get separate frequencies for left and right
+        freqs_left = torch.einsum('i,j->ij', t, self.freq_left)
+        freqs_right = torch.einsum('i,j->ij', t, self.freq_right)
+
+        # Apply frequencies
+        x_left, x_right = x[..., :self.dim//2], x[..., self.dim//2:]
+        x_left = x_left * freqs_left.cos() - x_right * freqs_left.sin()
+        x_right = x_left * freqs_right.sin() + x_right * freqs_right.cos()
+
+        # Combine the left and right parts back
+        x = torch.cat([x_left, x_right], dim=-1)
+
+        return x
+
+class ShortRope(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.n = config.shortrope_length
+        self.dim = config.n_embd
+
+        # Generate freqs of size n rather than full dim
+        self.register_buffer('freq_left', (10000 ** (torch.arange(0, self.n//2).float() / self.n//2)))
+        self.register_buffer('freq_right', (10000 ** (torch.arange(0, self.n//2).float() / self.n//2)))
+
+    def forward(self, x):
+        # Step 1: Get the input tensor shape
+        batch_size, seq_len, _ = x.shape
+
+        # Step 2: Split the input tensor into unrotated and rotated sections
+        x_unrotated = x[..., :-self.n]  # All but the last n dimensions
+        x_rotated = x[..., -self.n:]    # Only the last n dimensions
+
+        # Step 3: Generate rotation frequencies
+        t = torch.arange(self.n, device=x.device)
+        freqs_left = torch.einsum('i,j->ij', t, self.freq_left)
+        freqs_right = torch.einsum('i,j->ij', t, self.freq_right)
+
+        # Calculate how many times to repeat freqs along the sequence length
+        num_repeats = seq_len // self.n + int(seq_len % self.n != 0)
+
+        # Repeat the frequency tensors to match the sequence length
+        freqs_left = freqs_left.repeat(batch_size, num_repeats, 1)
+        freqs_right = freqs_right.repeat(batch_size, num_repeats, 1)
+
+        # Trim the excess elements so the freqs tensors match the sequence length
+        freqs_left = freqs_left[:, :seq_len, :]
+        freqs_right = freqs_right[:, :seq_len, :]
+
+        # Step 4: Process the x_rotated section
+        x_left = x_rotated[..., :self.n//2]
+        x_right = x_rotated[..., self.n//2:]
+
+        # Apply the cosine and sine rotations
+        x_left = x_left * freqs_left.cos() - x_right * freqs_left.sin()
+        x_right = x_left * freqs_right.sin() + x_right * freqs_right.cos()
+
+        # Invert the order of the right tensor's last dimension and negate it
+        x_right = torch.flip(x_right, dims=[-1]) * -1
+
+        # Combine the left and right rotated sections
+        x_rotated = torch.cat([x_left, x_right], dim=-1)
+
+        # Step 5: Combine the rotated and unrotated sections
+        x = torch.cat([x_unrotated, x_rotated], dim=-1)
+
+        return x
+
+
 class CausalSelfAttention(nn.Module):
 
     def __init__(self, config):
@@ -167,6 +252,14 @@ def __init__(self, config):
         # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0
         self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
 
+        # Rotary Positional Embeddings
+        self.rotary_emb = None
+        if config.use_rotary_embeddings:
+            if config.rope_variant == "rope":
+                self.rotary_emb = RotaryEmbedding(config)
+            if config.rope_variant == "shortrope":
+                self.rotary_emb = ShortRope(config)
+
         # Softmax Variant Selection
         self.use_softmax_variant = config.use_softmax_variant
         self.softmax_variant = config.softmax_variant
@@ -208,6 +301,8 @@ def __init__(self, config):
                                         .view(1, 1, config.block_size, config.block_size))
 
     def forward(self, x):
+        if self.rotary_emb is not None:
+          x = self.rotary_emb(x)
         B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
 
         # calculate query, key, values for all heads in batch and move head forward to be the batch dim
@@ -303,6 +398,12 @@ class GPTConfig:
     constantmax_constant: int = 1000 # denominator to utilize for Constantmax
     strongermax_strength: int = 2 # Softermax Option active is softermax selected - True: uses (x - x_max) normalization; False: removes normalization (potential overflow)
 
+    # Positional Embeddings Variations
+    use_rotary_embeddings: bool = True # If True, uses rotary embeddings, else use conventional absolute position encoding
+    rope_variant: str = "shortrope" # options: "shortrope", "rope
+    shortrope_length: int = 8 # number of embeddings to use in shortrope
+    use_abs_pos_embeddings: bool = True # If True, uses rotary embeddings, else use conventional absolute position encoding
+
     # Layernorm Alternatives and Options
     use_rmsnorm: bool = True # Add option for RMSNorm in place of LayerNorm: https://arxiv.org/abs/1910.07467
     use_relu: bool = False #True: relu squared, False: do not utilize
@@ -399,8 +500,12 @@ def forward(self, idx, targets=None):
 
         # forward the GPT model itself
         tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
-        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd)
-        x = self.transformer.drop(tok_emb + pos_emb)
+        x = None
+        if self.config.use_abs_pos_embeddings:
+          pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd)
+          x = self.transformer.drop(tok_emb + pos_emb)
+        else:
+          x = self.transformer.drop(tok_emb)
         for block in self.transformer.h:
             x = block(x)
         x = self.transformer.ln_f(x)
diff --git a/train.py b/train.py
index 84f9745772..59564213eb 100644
--- a/train.py
+++ b/train.py
@@ -54,6 +54,12 @@ def parse_args():
     # Norm variations
     model_group.add_argument('--use_rmsnorm', default=True, action=argparse.BooleanOptionalAction)
 
+    # Positional Embedding variations
+    model_group.add_argument('--use_rotary_embeddings', default=True, action=argparse.BooleanOptionalAction)
+    model_group.add_argument("--rope_variant", type=str, default="rope", choices=["shortrope", "rope"])
+    model_group.add_argument("--shortrope_length", type=int, default="16", help="number of embeddings to use with rope, must be <= length, and be even")
+    model_group.add_argument('--use_abs_pos_embeddings', default=False, action=argparse.BooleanOptionalAction)
+
     # Softmax variations
     model_group.add_argument('--use_softmax_variant', default=False, action=argparse.BooleanOptionalAction)
     model_group.add_argument("--softmax_variant", type=str, default="softermax", choices=["polymax", "strongermax", "softermax", "sigsoftmax", "sigsoftmax_base2"])