diff --git a/vllm/model_executor/models/transformers.py b/vllm/model_executor/models/transformers.py
index f7ced6134da52..5ad0482330ecd 100644
--- a/vllm/model_executor/models/transformers.py
+++ b/vllm/model_executor/models/transformers.py
@@ -17,6 +17,7 @@
 """Wrapper around `transformers` models"""
 from collections.abc import Iterable, Mapping
 from contextlib import contextmanager
+from pathlib import Path
 from typing import Literal, Optional, Union
 
 import regex as re
@@ -60,6 +61,21 @@ from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
 logger = init_logger(__name__)
 
 
+def get_feature_request_tip(
+    model: str,
+    trust_remote_code: bool,
+) -> str:
+    hf_url = f"a discussion at https://huggingface.co/{model}/discussions/new"
+    gh_url = "an issue at https://github.com/huggingface/transformers/issues/new/choose"
+    url = hf_url if trust_remote_code else gh_url
+    prefix = f"Please open {url} to request support for this feature. "
+    if Path(model).exists():
+        prefix = ""
+    doc_url = "https://docs.vllm.ai/en/latest/models/supported_models.html#writing-custom-models"
+    tip = f"See {doc_url} for instructions on how to add support yourself."
+    return f"{prefix}{tip}"
+
+
 def vllm_flash_attention_forward(
         # Transformers args
         module: torch.nn.Module,
@@ -480,8 +496,11 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
             return
 
         if not self.model.supports_pp_plan:
+            tip = get_feature_request_tip(self.model_config.model,
+                                          self.model_config.trust_remote_code)
             raise ValueError(
-                f"{type(self.model)} does not support pipeline parallel yet!")
+                f"{type(self.model)} does not support pipeline parallel. {tip}"
+            )
 
         module_lists = []
         module_list_idx = None
@@ -535,8 +554,10 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
         models_with_tp_plan = filter(supports_tp_plan, pretrained_models)
 
         if not any(models_with_tp_plan) and self.tp_size > 1:
+            tip = get_feature_request_tip(self.model_config.model,
+                                          self.model_config.trust_remote_code)
             raise ValueError(
-                f"{type(self.model)} does not support tensor parallel yet!")
+                f"{type(self.model)} does not support tensor parallel. {tip}")
 
         def _tensor_parallel(module: nn.Module,
                              prefix: str = "",