diff --git a/src/llamafactory/hparams/parser.py b/src/llamafactory/hparams/parser.py
index ec303655..00a4c72c 100644
--- a/src/llamafactory/hparams/parser.py
+++ b/src/llamafactory/hparams/parser.py
@@ -26,7 +26,7 @@ from transformers import HfArgumentParser, Seq2SeqTrainingArguments
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.training_args import ParallelMode
-from transformers.utils import is_torch_bf16_gpu_available
+from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_available
 from transformers.utils.versions import require_version
 
 from ..extras.constants import CHECKPOINT_NAMES
@@ -215,14 +215,15 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     ):
         raise ValueError("Please specify dataset for evaluation.")
 
-    if training_args.predict_with_generate and data_args.eval_dataset is None:
-        raise ValueError("Cannot use `predict_with_generate` if `eval_dataset` is None.")
+    if training_args.predict_with_generate:
+        if is_deepspeed_zero3_enabled():
+            raise ValueError("`predict_with_generate` is incompatible with DeepSpeed ZeRO-3.")
 
-    if training_args.predict_with_generate and finetuning_args.compute_accuracy:
-        raise ValueError("Cannot use `predict_with_generate` and `compute_accuracy` together.")
+        if data_args.eval_dataset is None:
+            raise ValueError("Cannot use `predict_with_generate` if `eval_dataset` is None.")
 
-    if training_args.predict_with_generate and is_deepspeed_zero3_enabled():
-        raise ValueError("`predict_with_generate` is incompatible with DeepSpeed ZeRO-3.")
+        if finetuning_args.compute_accuracy:
+            raise ValueError("Cannot use `predict_with_generate` and `compute_accuracy` together.")
 
     if training_args.do_train and model_args.quantization_device_map == "auto":
         raise ValueError("Cannot use device map for quantized models in training.")
@@ -231,7 +232,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
         raise ValueError("Please use scripts/pissa_init.py to initialize PiSSA in DeepSpeed ZeRO-3.")
 
     if finetuning_args.pure_bf16:
-        if not is_torch_bf16_gpu_available():
+        if not (is_torch_bf16_gpu_available() or (is_torch_npu_available() and torch.npu.is_bf16_supported())):
             raise ValueError("This device does not support `pure_bf16`.")
 
         if is_deepspeed_zero3_enabled():
diff --git a/src/llamafactory/model/model_utils/liger_kernel.py b/src/llamafactory/model/model_utils/liger_kernel.py
index 61de0be0..e40169ad 100644
--- a/src/llamafactory/model/model_utils/liger_kernel.py
+++ b/src/llamafactory/model/model_utils/liger_kernel.py
@@ -32,12 +32,16 @@ def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArgumen
 
     if getattr(config, "model_type", None) == "gemma":
         from liger_kernel.transformers import apply_liger_kernel_to_gemma as apply_liger_kernel
+    elif getattr(config, "model_type", None) == "gemma2":
+        from liger_kernel.transformers import apply_liger_kernel_to_gemma2 as apply_liger_kernel
     elif getattr(config, "model_type", None) == "llama":
         from liger_kernel.transformers import apply_liger_kernel_to_llama as apply_liger_kernel
     elif getattr(config, "model_type", None) == "mistral":
         from liger_kernel.transformers import apply_liger_kernel_to_mistral as apply_liger_kernel
     elif getattr(config, "model_type", None) == "mixtral":
         from liger_kernel.transformers import apply_liger_kernel_to_mixtral as apply_liger_kernel
+    elif getattr(config, "model_type", None) == "phi3":
+        from liger_kernel.transformers import apply_liger_kernel_to_phi3 as apply_liger_kernel
     elif getattr(config, "model_type", None) == "qwen2":
         from liger_kernel.transformers import apply_liger_kernel_to_qwen2 as apply_liger_kernel
     else: