diff --git a/training/weight_mapping.py b/training/weight_mapping.py
index b3f15b1..1abad45 100644
--- a/training/weight_mapping.py
+++ b/training/weight_mapping.py
@@ -17,6 +17,9 @@ import torch.nn as nn
 HIDDEN = 5120
 NUM_K_HEADS = 16
 NUM_V_HEADS = 48
+NUM_ATTN_HEADS = 24       # full attention q heads
+NUM_ATTN_KV_HEADS = 4     # full attention kv heads
+ATTN_HEAD_DIM = 256
 HEAD_K_DIM = 128
 HEAD_V_DIM = 128
 KEY_DIM = NUM_K_HEADS * HEAD_K_DIM     # 2048
@@ -26,6 +29,14 @@ NUM_LAYERS = 64
 CONV_KERNEL = 4
 CONV_DIM = KEY_DIM * 2 + VALUE_DIM     # 10240
 
+# Full attention QKV dimensions
+# Q uses 2x head_dim (512) vs KV head_dim (256) in Qwen3.5
+ATTN_Q_HEAD_DIM = ATTN_HEAD_DIM * 2             # 512
+ATTN_Q_DIM = NUM_ATTN_HEADS * ATTN_Q_HEAD_DIM   # 12288
+ATTN_K_DIM = NUM_ATTN_KV_HEADS * ATTN_HEAD_DIM  # 1024
+ATTN_V_DIM = NUM_ATTN_KV_HEADS * ATTN_HEAD_DIM  # 1024
+# Total: 12288 + 1024 + 1024 = 14336 = vLLM's qkv_proj.weight[0]
+
 
 def vllm_to_hf_views(vllm_params: dict[str, torch.Tensor]
                       ) -> dict[str, torch.Tensor]:
@@ -37,38 +48,50 @@ def vllm_to_hf_views(vllm_params: dict[str, torch.Tensor]
     hf_params = {}
 
     for name, tensor in vllm_params.items():
-        # Pass through non-merged params unchanged
-        if 'in_proj_qkvz' not in name and \
-           'in_proj_ba' not in name and \
-           'gate_up_proj' not in name:
-            hf_params[name] = tensor
-            continue
+        # vLLM and HF both use 'language_model.model.layers...' for Qwen3.5.
+        # HF checkpoint has 'model.' prefix but named_parameters() doesn't.
+        # Keep vLLM's names as-is — we'll match when loading into the HF model.
+        hf_name = name
 
         # Split merged projections into HF-style separate weights
         if 'in_proj_qkvz' in name:
-            # [key_dim*2 + value_dim*2, hidden] → qkv + z
-            prefix = name.replace('in_proj_qkvz', '')
-            qkv = tensor[:KEY_DIM * 2 + VALUE_DIM]  # [key_dim*2 + value_dim, hidden]
-            z = tensor[KEY_DIM * 2 + VALUE_DIM:]     # [value_dim, hidden]
+            # GDN: [key_dim*2 + value_dim*2, hidden] → qkv + z
+            prefix = hf_name.replace('in_proj_qkvz.weight', '')
+            qkv = tensor[:KEY_DIM * 2 + VALUE_DIM]
+            z = tensor[KEY_DIM * 2 + VALUE_DIM:]
             hf_params[prefix + 'in_proj_qkv.weight'] = qkv
             hf_params[prefix + 'in_proj_z.weight'] = z
 
         elif 'in_proj_ba' in name:
-            # [num_v_heads*2, hidden] → b + a
-            prefix = name.replace('in_proj_ba', '')
-            b = tensor[:NUM_V_HEADS]      # [num_v_heads, hidden]
-            a = tensor[NUM_V_HEADS:]       # [num_v_heads, hidden]
+            # GDN: [num_v_heads*2, hidden] → b + a
+            prefix = hf_name.replace('in_proj_ba.weight', '')
+            b = tensor[:NUM_V_HEADS]
+            a = tensor[NUM_V_HEADS:]
             hf_params[prefix + 'in_proj_b.weight'] = b
             hf_params[prefix + 'in_proj_a.weight'] = a
 
+        elif 'qkv_proj' in name:
+            # Full attention: [q_dim + k_dim + v_dim, hidden] → q + k + v
+            prefix = hf_name.replace('qkv_proj.weight', '')
+            q = tensor[:ATTN_Q_DIM]
+            k = tensor[ATTN_Q_DIM:ATTN_Q_DIM + ATTN_K_DIM]
+            v = tensor[ATTN_Q_DIM + ATTN_K_DIM:]
+            hf_params[prefix + 'q_proj.weight'] = q
+            hf_params[prefix + 'k_proj.weight'] = k
+            hf_params[prefix + 'v_proj.weight'] = v
+
         elif 'gate_up_proj' in name:
-            # [intermediate*2, hidden] → gate + up
-            prefix = name.replace('gate_up_proj', '')
-            gate = tensor[:INTERMEDIATE]   # [intermediate, hidden]
-            up = tensor[INTERMEDIATE:]     # [intermediate, hidden]
+            # MLP: [intermediate*2, hidden] → gate + up
+            prefix = hf_name.replace('gate_up_proj.weight', '')
+            gate = tensor[:INTERMEDIATE]
+            up = tensor[INTERMEDIATE:]
             hf_params[prefix + 'gate_proj.weight'] = gate
             hf_params[prefix + 'up_proj.weight'] = up
 
+        else:
+            # Pass through unchanged (norms, biases, out_proj, etc.)
+            hf_params[hf_name] = tensor
+
     return hf_params