diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py
index cfe9d025..a7c8c4a7 100644
--- a/server/text_generation_server/models/__init__.py
+++ b/server/text_generation_server/models/__init__.py
@@ -83,9 +83,6 @@ FLASH_ATTENTION = True
 
 try:
     from text_generation_server.models.flash_causal_lm import FlashCausalLM
-    from text_generation_server.models.transformers_flash_causal_lm import (
-        TransformersFlashCausalLM,
-    )
     from text_generation_server.models.vlm_causal_lm import VlmCausalLM
     from text_generation_server.models.mllama_causal_lm import MllamaCausalLM
     from text_generation_server.models.custom_modeling.flash_deepseek_v2_modeling import (
@@ -180,6 +177,14 @@ except ImportError as e:
 if MAMBA_AVAILABLE:
     __all__.append(Mamba)
 
+FLASH_TRANSFORMERS_BACKEND = True
+try:
+    from text_generation_server.models.transformers_flash_causal_lm import (
+        TransformersFlashCausalLM,
+    )
+except ImportError:
+    FLASH_TRANSFORMERS_BACKEND = False
+
 
 class ModelType(enum.Enum):
     DEEPSEEK_V2 = {
@@ -384,12 +389,8 @@ def get_model(
         transformers, modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[model_type]
     )
 
-    if FLASH_ATTENTION and transformers_model_class._supports_flex_attn:
+    if FLASH_TRANSFORMERS_BACKEND and transformers_model_class._supports_flex_attn:
         transformers_causal_lm_class = TransformersFlashCausalLM
-        if lora_adapter_ids is not None and len(lora_adapter_ids) > 0:
-            raise ValueError(
-                "Flash `Transformers` modeling backend does not support `lora_adapter_ids`."
-            )
 
     quantization_config = config_dict.get("quantization_config", None)
     if quantization_config is None: