diff --git a/main.py b/main.py
index 8d41c5f..9891232 100644
--- a/main.py
+++ b/main.py
@@ -19,7 +19,7 @@
     'block_size': 256,
     'world_size': 1,
     'model_name_or_path': 'Qwen/Qwen3-0.6B',
-    'enforce_eager': True,
+    'enforce_eager': False,
     'vocab_size': 151936,  # Fixed: was 151643, HF model uses 151936
     'hidden_size': 1024,
     'num_heads': 16,
diff --git a/src/myvllm/engine/model_runner.py b/src/myvllm/engine/model_runner.py
index 5dd66ce..457ab30 100644
--- a/src/myvllm/engine/model_runner.py
+++ b/src/myvllm/engine/model_runner.py
@@ -285,12 +285,14 @@ def prepare_prefill(self, seqs: list[Sequence]) -> torch.Tensor:
             seqlens_k.append(len(token_ids))
             cu_seqlens_q.append(cu_seqlens_q[-1] + seqlens_q[-1])
             cu_seqlens_k.append(cu_seqlens_k[-1] + seqlens_k[-1])
+            # by token generate slot_mapping
             if seq.block_table:
-                for i, block_id in enumerate(seq.block_table[seq.num_cached_blocks:]):
-                    if seq.num_cached_blocks + i != seq.num_blocks - 1:
-                        slot_mappings.extend(list(range(block_id * self.block_size, (block_id+1) * self.block_size)))
-                    else:
-                        slot_mappings.extend(list(range(block_id * self.block_size, block_id * self.block_size + seq.last_block_num_tokens)))
+                for pos in range(num_cached_tokens, len(token_ids)):
+                    block_idx = pos // self.block_size
+                    block_offset = pos % self.block_size
+                    block_id = seq.block_table[block_idx]
+                    slot_mappings.append(block_id * self.block_size + block_offset)
+                    
         if cu_seqlens_q[-1] < cu_seqlens_k[-1]:
             # pad block_tables
             all_block_tables = [seq.block_table for seq in seqs]
@@ -404,7 +406,7 @@ def run(self, seqs: list[Sequence], is_prefill: bool) -> list[int]:
     # (later use graph.replay() to run the captured graph)
     @torch.inference_mode()
     def capture_cudagraph(self) -> None:
-        max_bs = self.config['max_num_seqs']
+        max_bs = self.config['max_num_sequences']
         max_len = self.config['max_model_length']
         max_num_blocks = math.ceil(max_len / self.block_size)
         # for decoding, input is always of shape (batch_size, 1)
@@ -417,7 +419,7 @@ def capture_cudagraph(self) -> None:
         # where to read KV values in the cache
         block_tables = torch.zeros(max_bs, max_num_blocks, dtype=torch.int32, device=f'cuda:{self.rank}')
         # output logits
-        outputs = torch.zeros(max_bs, self.config['vocab_size'], device=f'cuda:{self.rank}')
+        outputs = torch.zeros(max_bs, self.config['hidden_size'], device=f'cuda:{self.rank}')
 
         # graphs to be captured for different batch sizes
         batch_sizes = [1, 2, 4, 8] + list(range(16, max_bs + 1, 16))
@@ -440,8 +442,9 @@ def capture_cudagraph(self) -> None:
 
             with torch.cuda.graph(graph, graph_pool):
                 outputs[:batch_size] = self.model(input_ids[:batch_size])
-                if graph_pool is None:
-                    graph_pool = graph.pool()
+
+            if graph_pool is None:
+                graph_pool = graph.pool()
             # store the captured graph
             self.graphs[batch_size] = graph