[misc] fix import error (#9299)

src/llamafactory/extras/packages.py

@@ -86,6 +86,14 @@ def is_rouge_available():
     return _is_package_available("rouge_chinese")
 
 
+def is_safetensors_available():
+    return _is_package_available("safetensors")
+
+
+def is_sglang_available():
+    return _is_package_available("sglang")
+
+
 def is_starlette_available():
     return _is_package_available("sse_starlette")
 
@@ -101,7 +109,3 @@ def is_uvicorn_available():
 
 def is_vllm_available():
     return _is_package_available("vllm")
-
-
-def is_sglang_available():
-    return _is_package_available("sglang")

src/llamafactory/hparams/model_args.py

@@ -20,15 +20,17 @@ from dataclasses import asdict, dataclass, field, fields
 from typing import Any, Literal, Optional, Union
 
 import torch
+from omegaconf import OmegaConf
 from transformers.training_args import _convert_str_dict
 from typing_extensions import Self
-from omegaconf import OmegaConf
 
 from ..extras.constants import AttentionFunction, EngineName, QuantizationMethod, RopeScaling
 from ..extras.logging import get_logger
 
+
 logger = get_logger(__name__)
 
+
 @dataclass
 class BaseModelArguments:
     r"""Arguments pertaining to the model."""
@@ -168,7 +170,7 @@ class BaseModelArguments:
         default="offload",
         metadata={"help": "Path to offload model weights."},
     )
-    use_cache: bool = field(
+    use_kv_cache: bool = field(
         default=True,
         metadata={"help": "Whether or not to use KV cache in generation."},
     )

src/llamafactory/hparams/training_args.py

@@ -81,6 +81,11 @@ class RayArguments:
 class TrainingArguments(RayArguments, Seq2SeqTrainingArguments):
     r"""Arguments pertaining to the trainer."""
 
+    overwrite_output_dir: bool = field(
+        default=False,
+        metadata={"help": "deprecated"},
+    )
+
     def __post_init__(self):
         Seq2SeqTrainingArguments.__post_init__(self)
         RayArguments.__post_init__(self)

src/llamafactory/model/model_utils/embedding.py

@@ -94,7 +94,7 @@ def _description_based_initialization(
             if len(valid_token_ids) == 0:
                 # Fallback: use mean of all existing embeddings
                 logger.warning_rank0(
-                    f"Description for token {i+1}/{num_new_tokens} contains no valid tokens. "
+                    f"Description for token {i + 1}/{num_new_tokens} contains no valid tokens. "
                     "Using mean of existing embeddings."
                 )
                 base_embedding = embed_weight[:-num_new_tokens].mean(dim=0)

src/llamafactory/model/model_utils/kv_cache.py

@@ -28,11 +28,11 @@ if TYPE_CHECKING:
 
 def configure_kv_cache(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
     if not is_trainable:
-        setattr(config, "use_cache", model_args.use_cache)
+        setattr(config, "use_cache", model_args.use_kv_cache)
         if hasattr(config, "text_config"):
-            setattr(config.text_config, "use_cache", model_args.use_cache)
+            setattr(config.text_config, "use_cache", model_args.use_kv_cache)
 
-        if model_args.use_cache:
+        if model_args.use_kv_cache:
             logger.info_rank0("KV cache is enabled for faster generation.")
         else:
             logger.info_rank0("KV cache is disabled.")

src/llamafactory/model/model_utils/moe.py

@@ -154,7 +154,17 @@ def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_t
     ]:
         setattr(text_config, "output_router_logits", True)
 
-    if model_type in ["ernie4_5_moe", "granitemoe", "jamba", "llama4", "mixtral", "olmoe", "phimoe", "qwen2_moe", "qwen3_moe"]:
+    if model_type in [
+        "ernie4_5_moe",
+        "granitemoe",
+        "jamba",
+        "llama4",
+        "mixtral",
+        "olmoe",
+        "phimoe",
+        "qwen2_moe",
+        "qwen3_moe",
+    ]:
         setattr(config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)
 
     elif text_config and getattr(text_config, "model_type", None) in ["qwen3_moe"]:

src/llamafactory/train/callbacks.py

@@ -26,16 +26,13 @@ import transformers
 from peft import PeftModel
 from transformers import PreTrainedModel, ProcessorMixin, TrainerCallback
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, has_length
-from transformers.utils import (
+from transformers.utils import SAFE_WEIGHTS_NAME, WEIGHTS_NAME
-    SAFE_WEIGHTS_NAME,
-    WEIGHTS_NAME,
-    is_safetensors_available,
-)
 from typing_extensions import override
 
 from ..extras import logging
 from ..extras.constants import TRAINER_LOG, V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from ..extras.misc import get_peak_memory, is_env_enabled, use_ray
+from ..extras.packages import is_safetensors_available
 
 
 if is_safetensors_available():