diff --git a/src/llamafactory/model/model_utils/attention.py b/src/llamafactory/model/model_utils/attention.py
index dfd90936..9021d277 100644
--- a/src/llamafactory/model/model_utils/attention.py
+++ b/src/llamafactory/model/model_utils/attention.py
@@ -28,7 +28,13 @@ if TYPE_CHECKING:
 logger = get_logger(__name__)
 
 
-def configure_attn_implementation(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
+def configure_attn_implementation(
+    config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool
+) -> None:
+    if getattr(config, "model_type", None) == "gemma2" and is_trainable:  # gemma2 adopts soft-cap attention
+        logger.warning("Gemma-2 models should use eager attention in training, change `flash_attn` to disabled.")
+        model_args.flash_attn = "disabled"
+
     if model_args.flash_attn == "auto":
         return
 
diff --git a/src/llamafactory/model/patcher.py b/src/llamafactory/model/patcher.py
index 24cd2601..4eae0bb4 100644
--- a/src/llamafactory/model/patcher.py
+++ b/src/llamafactory/model/patcher.py
@@ -67,7 +67,7 @@ def patch_config(
         use_jit_compile = os.environ.get("JIT_COMPILE", "0").lower() in ["true", "1"]
         torch.npu.set_compile_mode(jit_compile=use_jit_compile)
 
-    configure_attn_implementation(config, model_args)
+    configure_attn_implementation(config, model_args, is_trainable)
     configure_rope(config, model_args, is_trainable)
     configure_longlora(config, model_args, is_trainable)
     configure_quantization(config, tokenizer, model_args, init_kwargs)
diff --git a/src/llamafactory/webui/components/train.py b/src/llamafactory/webui/components/train.py
index f33c37ee..d4832dd3 100644
--- a/src/llamafactory/webui/components/train.py
+++ b/src/llamafactory/webui/components/train.py
@@ -54,7 +54,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         num_train_epochs = gr.Textbox(value="3.0")
         max_grad_norm = gr.Textbox(value="1.0")
         max_samples = gr.Textbox(value="100000")
-        compute_type = gr.Dropdown(choices=["fp16", "bf16", "fp32", "pure_bf16"], value="fp16")
+        compute_type = gr.Dropdown(choices=["bf16", "fp16", "fp32", "pure_bf16"], value="bf16")
 
     input_elems.update({learning_rate, num_train_epochs, max_grad_norm, max_samples, compute_type})
     elem_dict.update(