Fix conversion of https://huggingface.co/RedHatAI/Qwen3.8-27B-NVFP4

1. Per row FP8 wrongly fell to NVFP4 conversion, as NVFP4 checked on
   dims alone. Also check on dtype for FP8
2. Need to reshape FP8 QKV projections scales in the same way that
   weights are reshaped

conversion/base.py

@@ -595,6 +595,9 @@ class ModelBase:
         for name, value in new_tensors.items():
             self.model_tensors[name] = value
 
+    def _transform_fp8_scale(self, name: str, scale: Tensor) -> Tensor:
+        return scale
+
     def _prepare_fp8_e4m3_tensors(self):
         if self._fp8_as_q8:
             return
@@ -623,6 +626,8 @@ class ModelBase:
             if scale.numel() != 1:
                 continue
 
+            scale = self._transform_fp8_scale(weight_name, scale)
+
             weight_prefix = weight_name.removesuffix(".weight")
             # Transformers fine-grained FP8 uses activation_scale while ModelOpt uses input_scale.
             # Ref: https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/mistral3.py#L357-L361
@@ -971,9 +976,11 @@ class ModelBase:
             weight = LazyTorchTensor.to_eager(self.model_tensors[name]())
             scale = LazyTorchTensor.to_eager(self.model_tensors[scale_name]())
 
-            # Skip non-NVFP4 tensors (e.g. FP8 with per-channel 1D scales)
-            if scale.ndim < 2:
+            # Leave FP8 weights and their scales for _prepare_fp8_e4m3_tensors.
+            if weight.dtype in (torch.float8_e4m3fn, torch.float8_e5m2) or scale.ndim < 2:
                 continue
+            if weight.ndim != 2 or scale.shape[0] != weight.shape[0] or scale.shape[1] * 8 != weight.shape[1]:
+                raise ValueError(f"NVFP4 weight {name!r} has incompatible shapes: weight {list(weight.shape)}, scale {list(scale.shape)}")
 
             scale2 = LazyTorchTensor.to_eager(self.model_tensors.get(scale2_name, lambda: torch.tensor(1.0))())
             input_scale = LazyTorchTensor.to_eager(self.model_tensors.get(input_scale_name, lambda: torch.tensor(1.0))())

conversion/qwen.py

@@ -475,6 +475,24 @@ class _LinearAttentionVReorderBase(Qwen3NextModel):
         perm[dim], perm[dim + 1] = perm[dim + 1], perm[dim]
         return tensor.permute(*perm).contiguous().reshape(*shape)
 
+    def _transform_fp8_scale(self, name: str, scale: Tensor) -> Tensor:
+        num_k_heads = self.hparams.get("linear_num_key_heads", 0)
+        num_v_heads = self.hparams.get("linear_num_value_heads", 0)
+        if scale.numel() == 1 or num_k_heads == 0 or num_v_heads == 0 or num_k_heads == num_v_heads:
+            return scale
+
+        num_v_per_k = num_v_heads // num_k_heads
+        head_v_dim = self.hparams["linear_value_head_dim"]
+        if name.endswith(".linear_attn.in_proj_qkv.weight"):
+            qk_dim = 2 * self.hparams["linear_key_head_dim"] * num_k_heads
+            v_scale = self._reorder_v_heads(scale[qk_dim:], 0, num_k_heads, num_v_per_k, head_v_dim)
+            return torch.cat([scale[:qk_dim], v_scale])
+        if name.endswith(".linear_attn.in_proj_z.weight"):
+            return self._reorder_v_heads(scale, 0, num_k_heads, num_v_per_k, head_v_dim)
+        if name.endswith((".linear_attn.in_proj_a.weight", ".linear_attn.in_proj_b.weight")):
+            return self._reorder_v_heads(scale, 0, num_k_heads, num_v_per_k, 1)
+        return scale
+
     def _transform_nvfp4_weight(self, name: str, weight: Tensor, scale: Tensor) -> tuple[Tensor, Tensor]:
         if not name.endswith((
             ".linear_attn.in_proj_qkv.weight",