[Doc] Update V1 status for decoder-only embedding models (#19952)

Signed-off-by: Isotr0py <2037008807@qq.com>

vllm/model_executor/models/qwen2_rm.py

@@ -19,24 +19,12 @@ from vllm.model_executor.layers.pooler import Pooler, PoolingType, SimplePooler
 from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.sequence import IntermediateTensors, PoolerOutput
 
-from .interfaces import SupportsLoRA, SupportsPP, SupportsV0Only
+from .interfaces import SupportsLoRA, SupportsPP
 from .qwen2 import Qwen2Model
 from .utils import AutoWeightsLoader, maybe_prefix
 
 
-class ReLU(nn.Module):
-
-    def __init__(self):
-        super().__init__()
-        self.activation = nn.ReLU()
-
-    def forward(self, input):
-        input, _ = input
-        return self.activation(input)
-
-
-class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
-                           SupportsV0Only):
+class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -65,11 +53,13 @@ class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
         self.score = nn.Sequential(
             ColumnParallelLinear(config.hidden_size,
                                  config.hidden_size,
-                                 quant_config=quant_config),
-            ReLU(),
+                                 quant_config=quant_config,
+                                 return_bias=False),
+            nn.ReLU(),
             RowParallelLinear(config.hidden_size,
                               config.num_labels,
-                              quant_config=quant_config),
+                              quant_config=quant_config,
+                              return_bias=False),
         )
         self._pooler: SimplePooler
         self.make_empty_intermediate_tensors = (
@@ -87,7 +77,7 @@ class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         hidden_states = self.model(input_ids, positions, intermediate_tensors,
                                    inputs_embeds)
-        logits, _ = self.score(hidden_states)
+        logits = self.score(hidden_states)
         return logits
 
     def pooler(