diff --git a/src/llamafactory/hparams/parser.py b/src/llamafactory/hparams/parser.py
index 9b305016..a593bf45 100644
--- a/src/llamafactory/hparams/parser.py
+++ b/src/llamafactory/hparams/parser.py
@@ -199,6 +199,9 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
         if not is_torch_bf16_gpu_available():
             raise ValueError("This device does not support `pure_bf16`.")
 
+        if training_args.deepspeed:
+            raise ValueError("`pure_bf16` is incompatible with DeepSpeed.")
+
         if training_args.fp16 or training_args.bf16:
             raise ValueError("Turn off mixed precision training when using `pure_bf16`.")
 
diff --git a/src/llamafactory/model/adapter.py b/src/llamafactory/model/adapter.py
index 34518878..7caef9cc 100644
--- a/src/llamafactory/model/adapter.py
+++ b/src/llamafactory/model/adapter.py
@@ -289,16 +289,15 @@ def init_adapter(
             raise ValueError("Cannot initialize PiSSA adapter on quantized models.")
 
     # cast trainable parameters to float32 if:
-    # 1. is_trainable and quantization_bit is not None (qlora)
-    # 2. is_trainable and not deepspeed zero3 and not fsdp (zero3 or fsdp already in float32)
-    # 3. is_trainable and not pure_bf16 and not badam
+    # 1. is_trainable and not pure_bf16 and not badam and quantization_bit is not None (qlora)
+    # 2. is_trainable and not pure_bf16 and not badam and not zero3 and not fsdp (zero3 or fsdp already in fp32)
+    cast_trainable_params_to_fp32 = False
     if not is_trainable:
-        cast_trainable_params_to_fp32 = False
-    elif model_args.quantization_bit is None and (
-        is_deepspeed_zero3_enabled() or is_fsdp_enabled() or finetuning_args.pure_bf16 or finetuning_args.use_badam
-    ):
-        logger.info("ZeRO3/FSDP/PureBF16/BAdam detected, remaining trainable params as their original precision.")
-        cast_trainable_params_to_fp32 = False
+        pass
+    elif finetuning_args.pure_bf16 or finetuning_args.use_badam:
+        logger.info("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
+    elif model_args.quantization_bit is None and (is_deepspeed_zero3_enabled() or is_fsdp_enabled()):
+        logger.info("ZeRO3 / FSDP detected, remaining trainable params in float32.")
     else:
         logger.info("Upcasting trainable params to float32.")
         cast_trainable_params_to_fp32 = True
diff --git a/src/llamafactory/model/patcher.py b/src/llamafactory/model/patcher.py
index a53fde98..35153649 100644
--- a/src/llamafactory/model/patcher.py
+++ b/src/llamafactory/model/patcher.py
@@ -91,8 +91,8 @@ def patch_config(
 
     # cast data type of the model if:
     # 1. not deepspeed zero3 and not fsdp (keep zero3 or fsdp in float32)
-    # 2. fsdp + qlora
-    if model_args.quantization_bit is not None or (not is_deepspeed_zero3_enabled() and not is_fsdp_enabled()):
+    # 2. quantization_bit is not None (qlora)
+    if (not is_deepspeed_zero3_enabled() and not is_fsdp_enabled()) or model_args.quantization_bit is not None:
         init_kwargs["torch_dtype"] = model_args.compute_dtype
 
         if init_kwargs["low_cpu_mem_usage"]:  # device map requires low_cpu_mem_usage=True
diff --git a/src/llamafactory/webui/components/top.py b/src/llamafactory/webui/components/top.py
index 09d43ac8..18b9a7d2 100644
--- a/src/llamafactory/webui/components/top.py
+++ b/src/llamafactory/webui/components/top.py
@@ -49,10 +49,10 @@ def create_top() -> Dict[str, "Component"]:
             booster = gr.Radio(choices=["none", "flashattn2", "unsloth"], value="none", scale=3)
             visual_inputs = gr.Checkbox(scale=1)
 
-    model_name.change(get_model_info, [model_name], [model_path, template, visual_inputs], queue=False)
-    model_name.input(save_config, inputs=[lang, model_name], queue=False).then(
+    model_name.change(get_model_info, [model_name], [model_path, template, visual_inputs], queue=False).then(
         list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False
     )
+    model_name.input(save_config, inputs=[lang, model_name], queue=False)
     model_path.input(save_config, inputs=[lang, model_name, model_path], queue=False)
     finetuning_type.change(can_quantize, [finetuning_type], [quantization_bit], queue=False).then(
         list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False