diff --git a/models/language_model.py b/models/language_model.py
index 668330e4..7dff478d 100644
--- a/models/language_model.py
+++ b/models/language_model.py
@@ -113,62 +113,78 @@ def __init__(self, cfg):
             print("Warning: scaled dot product attention not available, using standard attention in LM.")
 
     def forward(self, x, cos, sin, attention_mask=None, kv_cache=None):
-        B, T, C = x.size()
+        B, T_curr, C = x.size() # T_curr is the sequence length of the current input x
 
-        q = self.q_proj(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)  # (B, n_heads, T, head_dim)
+        q_curr = self.q_proj(x).view(B, T_curr, self.n_heads, self.head_dim).transpose(1, 2)  # (B, n_heads, T_curr, head_dim)
+        k_curr = self.k_proj(x).view(B, T_curr, self.n_kv_heads, self.head_dim).transpose(1, 2) # (B, n_kv_heads, T_curr, head_dim)
+        v_curr = self.v_proj(x).view(B, T_curr, self.n_kv_heads, self.head_dim).transpose(1, 2) # (B, n_kv_heads, T_curr, head_dim)
+
+        # Apply rotary embeddings to the current q and k
+        q, k_rotated = apply_rotary_pos_embd(q_curr, k_curr, cos, sin)
 
         # Check if we can use cached keys and values
         if kv_cache is not None and kv_cache['key'] is not None:
-            k = kv_cache['key']  # (B, n_kv_heads, T_cached, head_dim)
-            v = kv_cache['value']  # (B, n_kv_heads, T_cached, head_dim)
-            # Compute keys and values for the new token only
-            new_k = self.k_proj(x[:, -1:, :]).view(B, 1, self.n_kv_heads, self.head_dim).transpose(1, 2)
-            new_v = self.v_proj(x[:, -1:, :]).view(B, 1, self.n_kv_heads, self.head_dim).transpose(1, 2)
-            # Append new keys and values to cache
-            k = torch.cat([k, new_k], dim=2)
-            v = torch.cat([v, new_v], dim=2)
+            # Concatenate with cached K, V
+            # k_rotated and v_curr are for the new token(s)
+            k_past = kv_cache['key']
+            v_past = kv_cache['value']
+            k = torch.cat([k_past, k_rotated], dim=2)
+            v = torch.cat([v_past, v_curr], dim=2)
         else:
-            # Compute keys and values for all tokens
-            k = self.k_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)  # (B, n_kv_heads, T, head_dim)
-            v = self.v_proj(x).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)  # (B, n_kv_heads, T, head_dim)
-
-        # Update KV cache
+            # No cache, this is the first pass (prefill)
+            k = k_rotated
+            v = v_curr
+        
         new_kv_cache = {'key': k, 'value': v}
 
-        # Use precomputed positional embeddings
-        q, k = apply_rotary_pos_embd(q, k, cos, sin)
-
-        k = k.repeat_interleave(self.n_kv_groups, dim=1)
-        v = v.repeat_interleave(self.n_kv_groups, dim=1)
+        # Repeat K, V for Grouped Query Attention
+        k_exp = k.repeat_interleave(self.n_kv_groups, dim=1) # (B, n_heads, T_kv, head_dim)
+        v_exp = v.repeat_interleave(self.n_kv_groups, dim=1) # (B, n_heads, T_kv, head_dim)
+        
+        T_kv = k_exp.size(2) # Total sequence length of keys/values
 
-        # Process attention mask if provided
+        # Prepare attention mask for SDPA or manual path
+        # attention_mask is (B, T_kv_total_length), 1 for attend, 0 for pad
+        additive_attn_mask = None
         if attention_mask is not None:
-            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)  # [B, 1, 1, T]
-            padding_mask = (attention_mask == 0).transpose(-1, -2)
-            attention_mask = (1.0 - attention_mask) * torch.finfo(q.dtype).min
+            # The current `attention_mask` parameter is assumed to be `[B, total_sequence_length_kv]`
+            # Let's make it `[B, 1, 1, T_kv]` for SDPA.
+            mask_for_keys = attention_mask[:, :T_kv] # Ensure mask matches key length [B, T_kv]
+            additive_attn_mask = (1.0 - mask_for_keys.unsqueeze(1).unsqueeze(2).float()) * torch.finfo(q.dtype).min
+            # This additive_attn_mask shape is [B, 1, 1, T_kv]
 
         if self.sdpa:
+            is_causal_sdpa = (kv_cache is None and T_curr > 1) # True only for prefill of a sequence
+                                                            # Not for single token decode, even if T_curr=1 then
+            
+            # When T_curr=1 (decode) and T_kv > 1, is_causal_sdpa is False.
+            # additive_attn_mask [B,1,1,T_kv] will mask out padded KV elements.
+            # Attention is for q (1 token) to all KVs. No further causal masking needed within this step by SDPA.
             y = torch.nn.functional.scaled_dot_product_attention(
-                q, k, v,
-                attn_mask=attention_mask,
+                q, k_exp, v_exp,
+                attn_mask=additive_attn_mask, 
                 dropout_p=self.dropout if self.training else 0.0,
-                is_causal=True # LM attention is causal (masked)
+                is_causal=is_causal_sdpa 
             )
         else:
-            attn = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(self.head_dim)
-            causal_mask = torch.tril(torch.ones(T, T, device=x.device)).view(1, 1, T, T)
-            attn = attn.masked_fill(causal_mask == 0, float('-inf'))
-            if attention_mask is not None:
-                attn = attn + attention_mask 
+            # Manual attention implementation
+            attn = torch.matmul(q, k_exp.transpose(2, 3)) / math.sqrt(self.head_dim) # (B, n_heads, T_curr, T_kv)
+            
+            # Causal mask for prefill where T_curr == T_kv and T_curr > 1
+            if kv_cache is None and T_curr > 1 and T_curr == T_kv:
+                # This creates a lower triangular mask for square attention (prefill)
+                causal_mask_val = torch.tril(torch.ones(T_curr, T_curr, device=x.device, dtype=torch.bool)).view(1, 1, T_curr, T_curr)
+                attn = attn.masked_fill(~causal_mask_val, float('-inf'))
+
+            if additive_attn_mask is not None: # Additive padding mask
+                # additive_attn_mask is [B,1,1,T_kv], needs to be broadcast to [B, n_heads, T_curr, T_kv]
+                attn = attn + additive_attn_mask 
 
             attn = F.softmax(attn, dim=-1)
             attn = self.attn_dropout(attn)
-            y = attn @ v
+            y = attn @ v_exp
             
-            if attention_mask is not None:
-                y = y.masked_fill(padding_mask, 0.0) # Zero out the padded positions in the output
-
-        y = y.transpose(1, 2).contiguous().view(B, T, C)  
+        y = y.transpose(1, 2).contiguous().view(B, T_curr, C)
         y = self.out_proj(y)
         y = self.resid_dropout(y)
 
@@ -245,58 +261,76 @@ def _init_weights(self, module):
         elif isinstance(module, RMSNorm):
             module.weight.data.fill_(1.0)
 
-    def forward(self, x, attention_mask=None, kv_cache=None):
+    def forward(self, x, attention_mask=None, kv_cache=None, start_pos=0):
         if self.lm_use_tokens:
-            x = self.token_embedding(x) # Only embed the inputs when using tokens
-        
-        B , T, _ = x.size()
+            x = self.token_embedding(x)
+
+        # T_curr is the length of the current input sequence
+        B, T_curr, _ = x.size()
         
-        # Note: You could also cache these input embeddings if you want to avoid recomputing them
-        position_ids = torch.arange(T, device=x.device).unsqueeze(0).expand(B, -1) # Create position ids [0, 1, 2, ..., seq_len-1]
-        cos, sin = self.rotary_embd(position_ids) # Get rotary position embeddings
+        # Create position_ids for the current sequence based on start_pos
+        current_position_ids = torch.arange(start_pos, start_pos + T_curr, device=x.device).unsqueeze(0).expand(B, -1)
+        cos, sin = self.rotary_embd(current_position_ids) # Get rotary position embeddings for current tokens
 
         # Initialize new KV cache if none provided
+        new_kv_cache_list = []
         if kv_cache is None:
             kv_cache = [None] * len(self.blocks)
-        new_kv_cache = []
 
         for i, block in enumerate(self.blocks):
             x, block_kv_cache = block(x, cos, sin, attention_mask, kv_cache[i])
-            new_kv_cache.append(block_kv_cache)
+            new_kv_cache_list.append(block_kv_cache)
 
         x = self.norm(x)
 
-        if self.lm_use_tokens:
-            x = self.head(x) # Compute logits if we are using tokens, otherwise stay in the embedding space
+        # Compute logits if we are using tokens, otherwise stay in the embedding space
+        if self.lm_use_tokens: 
+            x = self.head(x) 
 
-        return x, new_kv_cache
+        return x, new_kv_cache_list
 
 
-    @torch.no_grad()
+    @torch.inference_mode()
     def generate(self, inputs, max_new_tokens=20):
         # Add batch dimension if needed
         if inputs.dim() == 1:
             inputs = inputs.unsqueeze(0)
-            
-        generated = inputs.clone()
-        
-        for _ in range(max_new_tokens):
-            # Forward pass through the model
-            outputs = self.forward(generated)
-            last_output = outputs[:, -1, :]
-
+        generated_outputs = inputs.clone()
+
+        prompt_output, kv_cache_list = self.forward(
+            generated_outputs, 
+            attention_mask=None,
+            kv_cache=None,
+            start_pos=0
+        )
+        last_output = prompt_output[:, -1, :]
+
+        # Decode Phase with KV cache
+        for i in range(max_new_tokens):
             if self.lm_use_tokens:
                 # Now the model outputs logits
-                next_token = torch.argmax(last_output, dim=-1, keepdim=True)
-                generated = torch.cat((generated, next_token), dim=-1)
+                next_output = torch.argmax(last_output, dim=-1, keepdim=True)
             else:
                 # Now the model outputs embeddings
-                next_token_embedding = last_output.unsqueeze(1)  # Shape: [batch_size, 1, hidden_dim]
-                generated = torch.cat((generated, next_token_embedding), dim=1)
+                next_output = last_output.unsqueeze(1)
+
+            generated_outputs = torch.cat((generated_outputs, next_output), dim=1)
             
-            #Note: You could enable the generation to break earlier than max_new_tokens when it detects a eos token, but this does not work in batched generation (output tensors need to have the same size)
+            # The token being processed is `next_token`. Its position is `generated_outputs.size(1) - 1`.
+            current_token_start_pos = generated_outputs.size(1) - 1
+
+            if i == max_new_tokens - 1: 
+                break
+
+            decode_step_output, kv_cache_list = self.forward(
+                next_output, 
+                attention_mask=None,
+                kv_cache=kv_cache_list,
+                start_pos=current_token_start_pos
+            )
+            last_output = decode_step_output[:, -1, :] 
     
-        return generated
+        return generated_outputs
 
     # Load the model from a pretrained HuggingFace model (we don't want to have to train the Language Backbone from scratch)
     @classmethod
diff --git a/models/vision_language_model.py b/models/vision_language_model.py
index 545545c5..67254137 100644
--- a/models/vision_language_model.py
+++ b/models/vision_language_model.py
@@ -59,59 +59,81 @@ def forward(self, input_ids, image, attention_mask=None, targets=None):
 
         return logits, loss
 
-    @torch.no_grad()
-    def generate(self, input_ids, image, attention_mask=None, max_new_tokens=5, top_k=50, top_p=0.9, temperature=0.5, greedy=False):
-        # Process image through vision encoder and projection
-        image_embd = self.vision_encoder(image)
-        image_embd = self.MP(image_embd)
-        
-        # Embed initial tokens
-        token_embd = self.decoder.token_embedding(input_ids)
-        
-        # Concatenate image embeddings with token embeddings
-        combined_embd = torch.cat((image_embd, token_embd), dim=1)
+    @torch.inference_mode()
+    def generate(self, input_ids, image, max_new_tokens=5, top_k=50, top_p=0.9, temperature=0.5, greedy=False):
+        # input_ids: [B, T_prompt_text]
+        # image: [B, C, H, W]
+        # max_new_tokens: int, maximum number of tokens to generate
 
-        batch_size = image_embd.size(0)
-        img_seq_len = image_embd.size(1)
-        # Adjust attention mask to account for image tokens
-        if attention_mask is not None:
-            # Create mask of 1s for image tokens (all image tokens should be attended to)
-            image_attention_mask = torch.ones((batch_size, img_seq_len), device=attention_mask.device, dtype=attention_mask.dtype)
-            attention_mask = torch.cat((image_attention_mask, attention_mask), dim=1)
+        B = image.size(0)
+
+        # 1. Process image
+        image_embd = self.vision_encoder(image) # [B, T_img, D_model]
+        image_embd = self.MP(image_embd)      # [B, T_img, D_lm]
+
+        # 2. Embed initial text prompt tokens
+        prompt_token_embeds = self.decoder.token_embedding(input_ids) # [B, T_prompt_text, D_lm]
+
+        # 3. Combine image and text prompt embeddings for prefill
+        initial_combined_embeds = torch.cat((image_embd, prompt_token_embeds), dim=1) # [B, T_img + T_prompt_text, D_lm]
+        current_total_seq_len = initial_combined_embeds.size(1)
         
-        # Initialize KV cache: List to store key and value tensors for each block
-        kv_cache = [None] * len(self.decoder.blocks)
+        # --- Multimodal Prefill Phase ---
+        prefill_output, kv_cache_list = self.decoder.forward(
+            initial_combined_embeds,
+            attention_mask=None,
+            kv_cache=None,
+            start_pos=0
+        )
         
-        # Generate tokens one by one
-        outputs = combined_embd
-        generated_tokens = torch.zeros((batch_size, max_new_tokens), device=input_ids.device, dtype=input_ids.dtype)
+        last_token_output_from_prefill = prefill_output[:, -1, :] 
         
-        for i in range(max_new_tokens):
-            # Pass KV cache to decoder for efficient generation
-            model_out, kv_cache = self.decoder(outputs, attention_mask, kv_cache=kv_cache)
+        if not self.decoder.lm_use_tokens:
+            current_logits = self.decoder.head(last_token_output_from_prefill) 
+        else:
+            current_logits = last_token_output_from_prefill 
+
+        # Store newly generated token IDs
+        newly_generated_ids_list = []
+
+        # --- Decode Phase with Sampling tokens one by one ---
+        for _ in range(max_new_tokens):
+            if greedy:
+                next_token_id = torch.argmax(current_logits, dim=-1, keepdim=True)
+            else:
+                filtered_logits = top_k_top_p_filtering(current_logits, top_k=top_k, top_p=top_p)
+                probs = torch.softmax(filtered_logits / temperature, dim=-1)
+                next_token_id = torch.multinomial(probs, num_samples=1)
+            
+            newly_generated_ids_list.append(next_token_id)
             
-            last_token_logits = model_out[:, -1, :]
+            # Embed the newly generated token
+            next_token_embed = self.decoder.token_embedding(next_token_id) # [B, 1, D_lm]
+            
+            # The start_pos for the new token is the current total sequence length *before* adding this new token
+            current_token_start_pos = current_total_seq_len
+            current_total_seq_len += 1
+
+            # Call decoder.forward with the new token's embedding and the updated KV cache
+            decode_step_output, kv_cache_list = self.decoder.forward(
+                next_token_embed,
+                attention_mask=None, # Autoregressive, so no explicit mask beyond KV cache structure
+                kv_cache=kv_cache_list, # Pass the updated cache
+                start_pos=current_token_start_pos
+            )
+            
+            last_token_output = decode_step_output[:, -1, :] 
             
             # Apply head to get logits (if model is in embedding mode)
             if not self.decoder.lm_use_tokens:
-                last_token_logits = self.decoder.head(last_token_logits)
-            if greedy:
-                next_token = torch.argmax(last_token_logits, dim=-1, keepdim=True)
+                current_logits = self.decoder.head(last_token_output)
             else:
-                filtered_logits = top_k_top_p_filtering(last_token_logits, top_k=top_k, top_p=top_p)
-                probs = torch.softmax(filtered_logits/temperature, dim=-1)
-                next_token = torch.multinomial(probs, num_samples=1)
-                
-            generated_tokens[:, i] = next_token.squeeze(-1)
-            
-            # Convert to embedding and append
-            next_embd = self.decoder.token_embedding(next_token)
-            outputs = torch.cat((outputs, next_embd), dim=1)
-
-            if attention_mask is not None:
-                attention_mask = torch.cat((attention_mask, torch.ones((batch_size, 1), device=attention_mask.device)), dim=1)
+                current_logits = last_token_output
         
-        return generated_tokens
+        if not newly_generated_ids_list: # Handle case where max_new_tokens might be 0
+             return torch.empty((B,0), dtype=torch.long, device=input_ids.device)
+
+        return torch.cat(newly_generated_ids_list, dim=1)
 
     @classmethod
     def from_pretrained(