[misc] update format (#7277)

.github/ISSUE_TEMPLATE/1-bug-report.yml

@@ -47,8 +47,6 @@ body:
       description: |
         Please provide entry arguments, error messages and stack traces that reproduces the problem.
         请提供入口参数，错误日志以及异常堆栈以便于我们复现问题。
-        Remember to wrap your log messages with \`\`\`.
-        请务必使用 Markdown 标签 \`\`\` 来包裹您的日志信息。
 
       value: |
         ```text

.github/workflows/tests.yml

@@ -58,6 +58,10 @@ jobs:
         run: |
           make style && make quality
 
+      - name: Check license
+        run: |
+          make license
+
       - name: Test with pytest
         run: |
           make test

Makefile

@@ -1,4 +1,4 @@
-.PHONY: build commit quality style test
+.PHONY: build commit license quality style test
 
 check_dirs := scripts src tests setup.py
 
@@ -9,6 +9,9 @@ commit:
 	pre-commit install
 	pre-commit run --all-files
 
+license:
+	python3 tests/check_license.py $(check_dirs)
+
 quality:
 	ruff check $(check_dirs)
 	ruff format --check $(check_dirs)

data/belle_multiturn/belle_multiturn.py

@@ -1,3 +1,18 @@
+# Copyright 2025 the LlamaFactory team.
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 import os
 

data/hh_rlhf_en/hh_rlhf_en.py

@@ -1,3 +1,18 @@
+# Copyright 2025 the LlamaFactory team.
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 import os
 

data/ultra_chat/ultra_chat.py

@@ -1,3 +1,18 @@
+# Copyright 2025 the LlamaFactory team.
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 import os
 

evaluation/ceval/ceval.py

@@ -1,3 +1,4 @@
+# Copyright 2025 the LlamaFactory team.
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

evaluation/cmmlu/cmmlu.py

@@ -1,3 +1,4 @@
+# Copyright 2025 the LlamaFactory team.
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

evaluation/mmlu/mmlu.py

@@ -1,3 +1,4 @@
+# Copyright 2025 the LlamaFactory team.
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

scripts/api_example/test_toolcall.py

@@ -14,7 +14,6 @@
 
 import json
 import os
-from collections.abc import Sequence
 
 from openai import OpenAI
 from transformers.utils.versions import require_version
@@ -23,7 +22,7 @@ from transformers.utils.versions import require_version
 require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
 
 
-def calculate_gpa(grades: Sequence[str], hours: Sequence[int]) -> float:
+def calculate_gpa(grades: list[str], hours: list[int]) -> float:
     grade_to_score = {"A": 4, "B": 3, "C": 2}
     total_score, total_hour = 0, 0
     for grade, hour in zip(grades, hours):

scripts/stat_utils/cal_ppl.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import json
-from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import Any, Literal, Optional
 
@@ -35,7 +34,7 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
 
     train_on_prompt: bool = False
 
-    def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, torch.Tensor]:
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, torch.Tensor]:
         r"""Pad batched data to the longest sequence in the batch."""
         chosen_features = []
         for feature in features:

src/llamafactory/chat/base_engine.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from abc import ABC, abstractmethod
-from collections.abc import AsyncGenerator, Sequence
+from collections.abc import AsyncGenerator
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Literal, Optional, Union
 
@@ -63,12 +63,12 @@ class BaseEngine(ABC):
     @abstractmethod
     async def chat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> list["Response"]:
         r"""Get a list of responses of the chat model."""
@@ -77,12 +77,12 @@ class BaseEngine(ABC):
     @abstractmethod
     async def stream_chat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         r"""Get the response token-by-token of the chat model."""

src/llamafactory/chat/chat_model.py

@@ -1,4 +1,4 @@
-# Copyright 2024 THUDM and the LlamaFactory team.
+# Copyright 2025 THUDM and the LlamaFactory team.
 #
 # This code is inspired by the THUDM's ChatGLM implementation.
 # https://github.com/THUDM/ChatGLM-6B/blob/main/cli_demo.py
@@ -17,7 +17,7 @@
 
 import asyncio
 import os
-from collections.abc import AsyncGenerator, Generator, Sequence
+from collections.abc import AsyncGenerator, Generator
 from threading import Thread
 from typing import TYPE_CHECKING, Any, Optional
 
@@ -61,12 +61,12 @@ class ChatModel:
 
     def chat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> list["Response"]:
         r"""Get a list of responses of the chat model."""
@@ -77,12 +77,12 @@ class ChatModel:
 
     async def achat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> list["Response"]:
         r"""Asynchronously get a list of responses of the chat model."""
@@ -90,12 +90,12 @@ class ChatModel:
 
     def stream_chat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> Generator[str, None, None]:
         r"""Get the response token-by-token of the chat model."""
@@ -109,12 +109,12 @@ class ChatModel:
 
     async def astream_chat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         r"""Asynchronously get the response token-by-token of the chat model."""

src/llamafactory/chat/hf_engine.py

@@ -15,7 +15,7 @@
 import asyncio
 import concurrent.futures
 import os
-from collections.abc import AsyncGenerator, Sequence
+from collections.abc import AsyncGenerator
 from threading import Thread
 from typing import TYPE_CHECKING, Any, Callable, Optional, Union
 
@@ -78,12 +78,12 @@ class HuggingfaceEngine(BaseEngine):
         processor: Optional["ProcessorMixin"],
         template: "Template",
         generating_args: dict[str, Any],
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         input_kwargs: Optional[dict[str, Any]] = {},
     ) -> tuple[dict[str, Any], int]:
         mm_input_dict = {"images": [], "videos": [], "audios": [], "imglens": [0], "vidlens": [0], "audlens": [0]}
@@ -219,12 +219,12 @@ class HuggingfaceEngine(BaseEngine):
         processor: Optional["ProcessorMixin"],
         template: "Template",
         generating_args: dict[str, Any],
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         input_kwargs: Optional[dict[str, Any]] = {},
     ) -> list["Response"]:
         gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
@@ -274,12 +274,12 @@ class HuggingfaceEngine(BaseEngine):
         processor: Optional["ProcessorMixin"],
         template: "Template",
         generating_args: dict[str, Any],
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         input_kwargs: Optional[dict[str, Any]] = {},
     ) -> Callable[[], str]:
         gen_kwargs, _ = HuggingfaceEngine._process_args(
@@ -338,12 +338,12 @@ class HuggingfaceEngine(BaseEngine):
     @override
     async def chat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> list["Response"]:
         if not self.can_generate:
@@ -371,12 +371,12 @@ class HuggingfaceEngine(BaseEngine):
     @override
     async def stream_chat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         if not self.can_generate:

src/llamafactory/chat/vllm_engine.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import uuid
-from collections.abc import AsyncGenerator, AsyncIterator, Sequence
+from collections.abc import AsyncGenerator, AsyncIterator
 from typing import TYPE_CHECKING, Any, Optional, Union
 
 from typing_extensions import override
@@ -102,12 +102,12 @@ class VllmEngine(BaseEngine):
 
     async def _generate(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncIterator["RequestOutput"]:
         request_id = f"chatcmpl-{uuid.uuid4().hex}"
@@ -202,12 +202,12 @@ class VllmEngine(BaseEngine):
     @override
     async def chat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> list["Response"]:
         final_output = None
@@ -231,12 +231,12 @@ class VllmEngine(BaseEngine):
     @override
     async def stream_chat(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
-        images: Optional[Sequence["ImageInput"]] = None,
+        images: Optional[list["ImageInput"]] = None,
-        videos: Optional[Sequence["VideoInput"]] = None,
+        videos: Optional[list["VideoInput"]] = None,
-        audios: Optional[Sequence["AudioInput"]] = None,
+        audios: Optional[list["AudioInput"]] = None,
         **input_kwargs,
     ) -> AsyncGenerator[str, None]:
         generated_text = ""

src/llamafactory/data/collator.py

@@ -1,4 +1,4 @@
-# Copyright 2024 OpenAccess AI Collective and the LlamaFactory team.
+# Copyright 2025 OpenAccess AI Collective and the LlamaFactory team.
 #
 # This code is inspired by the OpenAccess AI Collective's axolotl library.
 # https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/src/axolotl/monkeypatch/utils.py
@@ -15,7 +15,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Literal, Optional
 
@@ -92,7 +91,7 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
         if self.template is None:
             raise ValueError("Template is required for MultiModalDataCollator.")
 
-    def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         batch_images, batch_videos, batch_audios = [], [], []
         batch_imglens, batch_vidlens, batch_audlens, batch_input_ids = [], [], [], []
         for feature in features:
@@ -205,7 +204,7 @@ class SFTDataCollatorWith4DAttentionMask(MultiModalDataCollatorForSeq2Seq):
     attn_implementation: Literal["eager", "sdpa", "flash_attention_2"] = "eager"
     compute_dtype: "torch.dtype" = torch.float32
 
-    def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         features = super().__call__(features)
         if self.block_diag_attn and self.attn_implementation != "flash_attention_2":
             features["attention_mask"] = prepare_4d_attention_mask(features["attention_mask"], self.compute_dtype)
@@ -221,7 +220,7 @@ class SFTDataCollatorWith4DAttentionMask(MultiModalDataCollatorForSeq2Seq):
 class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
     r"""Data collator for pairwise data."""
 
-    def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         r"""Pad batched data to the longest sequence in the batch.
 
         We generate 2 * n examples where the first n examples represent chosen examples and
@@ -247,7 +246,7 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
 class KTODataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
     r"""Data collator for KTO data."""
 
-    def __call__(self, features: Sequence[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
         target_features = []
         kl_features = []
         kto_tags = []

src/llamafactory/data/converter.py

@@ -14,7 +14,6 @@
 
 import os
 from abc import abstractmethod
-from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Optional, Union
 
@@ -37,7 +36,7 @@ class DatasetConverter:
     dataset_attr: "DatasetAttr"
     data_args: "DataArguments"
 
-    def _find_medias(self, medias: Union[Any, Sequence[Any]]) -> Optional[list[Any]]:
+    def _find_medias(self, medias: Union[Any, list[Any]]) -> Optional[list[Any]]:
         r"""Optionally concatenate media path to media dir when loading from local disk."""
         if not isinstance(medias, list):
             medias = [medias] if medias is not None else []

src/llamafactory/data/data_utils.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Sequence
 from enum import Enum, unique
 from typing import TYPE_CHECKING, Optional, TypedDict, Union
 
@@ -30,7 +29,7 @@ if TYPE_CHECKING:
 logger = logging.get_logger(__name__)
 
 
-SLOTS = Sequence[Union[str, set[str], dict[str, str]]]
+SLOTS = list[Union[str, set[str], dict[str, str]]]
 
 
 @unique

src/llamafactory/data/loader.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import os
-from collections.abc import Sequence
 from typing import TYPE_CHECKING, Literal, Optional, Union
 
 import numpy as np
@@ -157,7 +156,7 @@ def _load_single_dataset(
 
 
 def _get_merged_dataset(
-    dataset_names: Optional[Sequence[str]],
+    dataset_names: Optional[list[str]],
     model_args: "ModelArguments",
     data_args: "DataArguments",
     training_args: "Seq2SeqTrainingArguments",

src/llamafactory/data/mm_plugin.py

@@ -18,7 +18,6 @@
 import inspect
 import math
 import re
-from collections.abc import Sequence
 from copy import deepcopy
 from dataclasses import dataclass
 from io import BytesIO
@@ -83,9 +82,7 @@ if TYPE_CHECKING:
             pass
 
 
-def _get_paligemma_token_type_ids(
+def _get_paligemma_token_type_ids(imglens: list[int], seqlens: list[int], processor: "MMProcessor") -> list[list[int]]:
-    imglens: Sequence[int], seqlens: Sequence[int], processor: "MMProcessor"
-) -> list[list[int]]:
     r"""Get paligemma token type ids for computing loss.
 
     It is slightly different with the original token type ids where the prompt part is 0.
@@ -120,7 +117,7 @@ def _get_gemma3_token_type_ids(batch_ids: list[list[int]], processor: "MMProcess
     return batch_token_type_ids
 
 
-def _make_batched_images(images: Sequence["ImageObject"], imglens: list[int]) -> list[list["ImageObject"]]:
+def _make_batched_images(images: list["ImageObject"], imglens: list[int]) -> list[list["ImageObject"]]:
     r"""Make nested list of images."""
     batch_images = []
     for imglen in imglens:
@@ -140,9 +137,9 @@ class MMPluginMixin:
     def _validate_input(
         self,
         processor: Optional["MMProcessor"],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
     ) -> None:
         r"""Validate if this model accepts the input modalities."""
         image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
@@ -202,7 +199,7 @@ class MMPluginMixin:
         sample_frames = min(total_frames, video_maxlen, sample_frames)
         return np.linspace(0, total_frames - 1, sample_frames).astype(np.int32)
 
-    def _regularize_images(self, images: Sequence["ImageInput"], **kwargs) -> list["ImageObject"]:
+    def _regularize_images(self, images: list["ImageInput"], **kwargs) -> list["ImageObject"]:
         r"""Regularize images to avoid error. Including reading and pre-processing."""
         results = []
         for image in images:
@@ -223,7 +220,7 @@ class MMPluginMixin:
 
         return results
 
-    def _regularize_videos(self, videos: Sequence["VideoInput"], **kwargs) -> list[list["ImageObject"]]:
+    def _regularize_videos(self, videos: list["VideoInput"], **kwargs) -> list[list["ImageObject"]]:
         r"""Regularizes videos to avoid error. Including reading, resizing and converting."""
         results = []
         for video in videos:
@@ -241,7 +238,7 @@ class MMPluginMixin:
 
         return results
 
-    def _regularize_audios(self, audios: Sequence["AudioInput"], sampling_rate: float, **kwargs) -> list["NDArray"]:
+    def _regularize_audios(self, audios: list["AudioInput"], sampling_rate: float, **kwargs) -> list["NDArray"]:
         r"""Regularizes audios to avoid error. Including reading and resampling."""
         results = []
         for audio in audios:
@@ -257,9 +254,9 @@ class MMPluginMixin:
 
     def _get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: "MMProcessor",
         imglens: Optional[list[int]] = None,
     ) -> dict[str, "torch.Tensor"]:
@@ -335,10 +332,10 @@ class MMPluginMixin:
 class BasePlugin(MMPluginMixin):
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         r"""Pre-process input messages before tokenization for VLMs."""
@@ -349,9 +346,9 @@ class BasePlugin(MMPluginMixin):
         self,
         input_ids: list[int],
         labels: Optional[list[int]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         tokenizer: "PreTrainedTokenizer",
         processor: Optional["MMProcessor"],
     ) -> tuple[list[int], Optional[list[int]]]:
@@ -361,13 +358,13 @@ class BasePlugin(MMPluginMixin):
 
     def get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
-        imglens: Sequence[int],
+        imglens: list[int],
-        vidlens: Sequence[int],
+        vidlens: list[int],
-        audlens: Sequence[int],
+        audlens: list[int],
-        batch_ids: Sequence[list[int]],
+        batch_ids: list[list[int]],
         processor: Optional["MMProcessor"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         r"""Build batched multimodal inputs for VLMs.
@@ -392,10 +389,10 @@ class Gemma3Plugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -420,13 +417,13 @@ class Gemma3Plugin(BasePlugin):
     @override
     def get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
-        imglens: Sequence[int],
+        imglens: list[int],
-        vidlens: Sequence[int],
+        vidlens: list[int],
-        audlens: Sequence[int],
+        audlens: list[int],
-        batch_ids: Sequence[list[int]],
+        batch_ids: list[list[int]],
         processor: Optional["MMProcessor"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         self._validate_input(processor, images, videos, audios)
@@ -441,10 +438,10 @@ class LlavaPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -481,10 +478,10 @@ class LlavaNextPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -523,10 +520,10 @@ class LlavaNextVideoPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -586,10 +583,10 @@ class MiniCPMVPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -686,9 +683,9 @@ class MiniCPMVPlugin(BasePlugin):
     @override
     def _get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: "MMProcessor",
         **kwargs,
     ) -> dict[str, "torch.Tensor"]:
@@ -757,13 +754,13 @@ class MiniCPMVPlugin(BasePlugin):
     @override
     def get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
-        imglens: Sequence[int],
+        imglens: list[int],
-        vidlens: Sequence[int],
+        vidlens: list[int],
-        audlens: Sequence[int],
+        audlens: list[int],
-        batch_ids: Sequence[list[int]],
+        batch_ids: list[list[int]],
         processor: Optional["MMProcessor"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         self._validate_input(processor, images, videos, audios)
@@ -828,10 +825,10 @@ class MllamaPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -850,13 +847,13 @@ class MllamaPlugin(BasePlugin):
     @override
     def get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
-        imglens: Sequence[int],
+        imglens: list[int],
-        vidlens: Sequence[int],
+        vidlens: list[int],
-        audlens: Sequence[int],
+        audlens: list[int],
-        batch_ids: Sequence[list[int]],
+        batch_ids: list[list[int]],
         processor: Optional["MMProcessor"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         self._validate_input(processor, images, videos, audios)
@@ -885,10 +882,10 @@ class PaliGemmaPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -912,9 +909,9 @@ class PaliGemmaPlugin(BasePlugin):
         self,
         input_ids: list[int],
         labels: Optional[list[int]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         tokenizer: "PreTrainedTokenizer",
         processor: Optional["MMProcessor"],
     ) -> tuple[list[int], Optional[list[int]]]:
@@ -931,13 +928,13 @@ class PaliGemmaPlugin(BasePlugin):
     @override
     def get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
-        imglens: Sequence[int],
+        imglens: list[int],
-        vidlens: Sequence[int],
+        vidlens: list[int],
-        audlens: Sequence[int],
+        audlens: list[int],
-        batch_ids: Sequence[list[int]],
+        batch_ids: list[list[int]],
         processor: Optional["MMProcessor"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         self._validate_input(processor, images, videos, audios)
@@ -952,10 +949,10 @@ class PixtralPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -995,13 +992,13 @@ class PixtralPlugin(BasePlugin):
     @override
     def get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
-        imglens: Sequence[int],
+        imglens: list[int],
-        vidlens: Sequence[int],
+        vidlens: list[int],
-        audlens: Sequence[int],
+        audlens: list[int],
-        batch_ids: Sequence[list[int]],
+        batch_ids: list[list[int]],
         processor: Optional["MMProcessor"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         self._validate_input(processor, images, videos, audios)
@@ -1015,10 +1012,10 @@ class Qwen2AudioPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -1056,13 +1053,13 @@ class Qwen2AudioPlugin(BasePlugin):
     @override
     def get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
-        imglens: Sequence[int],
+        imglens: list[int],
-        vidlens: Sequence[int],
+        vidlens: list[int],
-        audlens: Sequence[int],
+        audlens: list[int],
-        batch_ids: Sequence[list[int]],
+        batch_ids: list[list[int]],
         processor: Optional["MMProcessor"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         self._validate_input(processor, images, videos, audios)
@@ -1090,7 +1087,7 @@ class Qwen2VLPlugin(BasePlugin):
 
     @override
     def _regularize_videos(
-        self, videos: Sequence["VideoInput"], **kwargs
+        self, videos: list["VideoInput"], **kwargs
     ) -> tuple[list[list["ImageObject"]], list[float]]:
         results, fps_per_video = [], []
         for video in videos:
@@ -1118,9 +1115,9 @@ class Qwen2VLPlugin(BasePlugin):
     @override
     def _get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: "MMProcessor",
     ) -> dict[str, "torch.Tensor"]:
         image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
@@ -1149,10 +1146,10 @@ class Qwen2VLPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)
@@ -1204,13 +1201,13 @@ class Qwen2VLPlugin(BasePlugin):
     @override
     def get_mm_inputs(
         self,
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
-        imglens: Sequence[int],
+        imglens: list[int],
-        vidlens: Sequence[int],
+        vidlens: list[int],
-        audlens: Sequence[int],
+        audlens: list[int],
-        batch_ids: Sequence[list[int]],
+        batch_ids: list[list[int]],
         processor: Optional["MMProcessor"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         self._validate_input(processor, images, videos, audios)
@@ -1229,10 +1226,10 @@ class VideoLlavaPlugin(BasePlugin):
     @override
     def process_messages(
         self,
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
         processor: Optional["MMProcessor"],
     ) -> list[dict[str, str]]:
         self._validate_input(processor, images, videos, audios)

src/llamafactory/data/parser.py

@@ -14,7 +14,6 @@
 
 import json
 import os
-from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import Any, Literal, Optional
 
@@ -91,7 +90,7 @@ class DatasetAttr:
                 self.set_attr(tag, attr["tags"])
 
 
-def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -> list["DatasetAttr"]:
+def get_dataset_list(dataset_names: Optional[list[str]], dataset_dir: str) -> list["DatasetAttr"]:
     r"""Get the attributes of the datasets."""
     if dataset_names is None:
         dataset_names = []

src/llamafactory/data/processor/__init__.py

@@ -1,3 +1,17 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from .feedback import FeedbackDatasetProcessor
 from .pairwise import PairwiseDatasetProcessor
 from .pretrain import PretrainDatasetProcessor

src/llamafactory/data/processor/feedback.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 from collections import defaultdict
-from collections.abc import Sequence
 from typing import TYPE_CHECKING, Any, Optional
 
 from ...extras import logging
@@ -31,14 +30,14 @@ logger = logging.get_logger(__name__)
 class FeedbackDatasetProcessor(DatasetProcessor):
     def _encode_data_example(
         self,
-        prompt: Sequence[dict[str, str]],
+        prompt: list[dict[str, str]],
-        response: Sequence[dict[str, str]],
+        response: list[dict[str, str]],
-        kl_response: Sequence[dict[str, str]],
+        kl_response: list[dict[str, str]],
         system: Optional[str],
         tools: Optional[str],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
     ) -> tuple[list[int], list[int], list[int], list[int], bool]:
         if response[0]["content"]:  # desired example
             kto_tag = True

src/llamafactory/data/processor/pairwise.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 from collections import defaultdict
-from collections.abc import Sequence
 from typing import TYPE_CHECKING, Any, Optional
 
 from ...extras import logging
@@ -31,13 +30,13 @@ logger = logging.get_logger(__name__)
 class PairwiseDatasetProcessor(DatasetProcessor):
     def _encode_data_example(
         self,
-        prompt: Sequence[dict[str, str]],
+        prompt: list[dict[str, str]],
-        response: Sequence[dict[str, str]],
+        response: list[dict[str, str]],
         system: Optional[str],
         tools: Optional[str],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
     ) -> tuple[list[int], list[int], list[int], list[int]]:
         chosen_messages = self.template.mm_plugin.process_messages(
             prompt + [response[0]], images, videos, audios, self.processor

src/llamafactory/data/processor/pretrain.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

src/llamafactory/data/processor/processor_utils.py

@@ -14,7 +14,6 @@
 
 import bisect
 from abc import ABC, abstractmethod
-from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Optional
 
@@ -46,7 +45,7 @@ class DatasetProcessor(ABC):
         ...
 
 
-def search_for_fit(numbers: Sequence[int], capacity: int) -> int:
+def search_for_fit(numbers: list[int], capacity: int) -> int:
     r"""Find the index of largest number that fits into the knapsack with the given capacity."""
     index = bisect.bisect(numbers, capacity)
     return -1 if index == 0 else (index - 1)

src/llamafactory/data/processor/supervised.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 from collections import defaultdict
-from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Optional
 
@@ -33,13 +32,13 @@ logger = logging.get_logger(__name__)
 class SupervisedDatasetProcessor(DatasetProcessor):
     def _encode_data_example(
         self,
-        prompt: Sequence[dict[str, str]],
+        prompt: list[dict[str, str]],
-        response: Sequence[dict[str, str]],
+        response: list[dict[str, str]],
         system: Optional[str],
         tools: Optional[str],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
     ) -> tuple[list[int], list[int]]:
         messages = self.template.mm_plugin.process_messages(prompt + response, images, videos, audios, self.processor)
         input_ids, labels = self.template.mm_plugin.process_token_ids(

src/llamafactory/data/processor/unsupervised.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 from collections import defaultdict
-from collections.abc import Sequence
 from typing import TYPE_CHECKING, Any, Optional
 
 from ...extras import logging
@@ -31,13 +30,13 @@ logger = logging.get_logger(__name__)
 class UnsupervisedDatasetProcessor(DatasetProcessor):
     def _encode_data_example(
         self,
-        prompt: Sequence[dict[str, str]],
+        prompt: list[dict[str, str]],
-        response: Sequence[dict[str, str]],
+        response: list[dict[str, str]],
         system: Optional[str],
         tools: Optional[str],
-        images: Sequence["ImageInput"],
+        images: list["ImageInput"],
-        videos: Sequence["VideoInput"],
+        videos: list["VideoInput"],
-        audios: Sequence["AudioInput"],
+        audios: list["AudioInput"],
     ) -> tuple[list[int], list[int]]:
         if len(response) == 1:
             messages = prompt + response

src/llamafactory/data/template.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Optional, Union
 
@@ -57,7 +56,7 @@ class Template:
     def encode_oneturn(
         self,
         tokenizer: "PreTrainedTokenizer",
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
     ) -> tuple[list[int], list[int]]:
@@ -73,7 +72,7 @@ class Template:
     def encode_multiturn(
         self,
         tokenizer: "PreTrainedTokenizer",
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str] = None,
         tools: Optional[str] = None,
     ) -> list[tuple[list[int], list[int]]]:
@@ -115,7 +114,7 @@ class Template:
     def _encode(
         self,
         tokenizer: "PreTrainedTokenizer",
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: Optional[str],
         tools: Optional[str],
     ) -> list[list[int]]:
@@ -316,7 +315,7 @@ class Llama2Template(Template):
     def _encode(
         self,
         tokenizer: "PreTrainedTokenizer",
-        messages: Sequence[dict[str, str]],
+        messages: list[dict[str, str]],
         system: str,
         tools: str,
     ) -> list[list[int]]:
@@ -391,7 +390,7 @@ def register_template(
     format_tools: Optional["Formatter"] = None,
     format_prefix: Optional["Formatter"] = None,
     default_system: str = "",
-    stop_words: Optional[Sequence[str]] = None,
+    stop_words: Optional[list[str]] = None,
     thought_words: Optional[tuple[str, str]] = None,
     efficient_eos: bool = False,
     replace_eos: bool = False,

src/llamafactory/eval/template.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Sequence
 from dataclasses import dataclass
 
 from ..data import Role
@@ -35,7 +34,7 @@ class EvalTemplate:
         return "".join([example["question"]] + candidates + [self.answer]), example["answer"]
 
     def format_example(
-        self, target_data: dict[str, str], support_set: Sequence[dict[str, str]], subject_name: str
+        self, target_data: dict[str, str], support_set: list[dict[str, str]], subject_name: str
     ) -> list[dict[str, str]]:
         r"""Convert dataset examples to messages."""
         messages = []

src/llamafactory/extras/env.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/commands/env.py

src/llamafactory/extras/logging.py

@@ -1,4 +1,4 @@
-# Copyright 2024 Optuna, HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 Optuna, HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/utils/logging.py

src/llamafactory/extras/misc.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's PEFT library.
 # https://github.com/huggingface/peft/blob/v0.10.0/src/peft/peft_model.py
@@ -17,7 +17,6 @@
 
 import gc
 import os
-from collections.abc import Sequence
 from typing import TYPE_CHECKING, Any, Literal, Union
 
 import torch
@@ -98,7 +97,7 @@ def check_dependencies() -> None:
         logger.warning_rank0_once("There are known bugs in transformers v4.46.0-v4.48.0, please use other versions.")
 
 
-def calculate_tps(dataset: Sequence[dict[str, Any]], metrics: dict[str, float], stage: Literal["sft", "rm"]) -> float:
+def calculate_tps(dataset: list[dict[str, Any]], metrics: dict[str, float], stage: Literal["sft", "rm"]) -> float:
     r"""Calculate effective tokens per second."""
     effective_token_num = 0
     for data in dataset:

src/llamafactory/extras/packages.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/utils/import_utils.py

src/llamafactory/hparams/data_args.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

src/llamafactory/hparams/model_args.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

src/llamafactory/hparams/parser.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

src/llamafactory/hparams/training_args.py

@@ -1,3 +1,17 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 from dataclasses import dataclass, field
 from typing import Literal, Optional, Union

src/llamafactory/model/model_utils/checkpointing.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc., Daniel Han-Chen & the Unsloth team and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc., Daniel Han-Chen & the Unsloth team and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's Transformers and PEFT library,
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/modeling_utils.py

src/llamafactory/model/model_utils/longlora.py

@@ -1,4 +1,4 @@
-# Copyright 2024 EleutherAI, HuggingFace Inc., Yukang Chen, and the LlamaFactory team.
+# Copyright 2025 EleutherAI, HuggingFace Inc., Yukang Chen, and the LlamaFactory team.
 #
 # This code is based on the EleutherAI's GPT-NeoX and the HuggingFace's Transformers libraries.
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py

src/llamafactory/model/model_utils/moe.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Sequence
 from typing import TYPE_CHECKING
 
 import torch
@@ -27,7 +26,7 @@ if TYPE_CHECKING:
     from ...hparams import ModelArguments
 
 
-def _set_z3_leaf_modules(model: "PreTrainedModel", leaf_modules: Sequence["torch.nn.Module"]) -> None:
+def _set_z3_leaf_modules(model: "PreTrainedModel", leaf_modules: list["torch.nn.Module"]) -> None:
     check_version("deepspeed>=0.13.0")
     from deepspeed.utils import set_z3_leaf_modules  # type: ignore
 

src/llamafactory/model/model_utils/packing.py

@@ -1,4 +1,4 @@
-# Copyright 2024 Musab Gultekin and the LlamaFactory team.
+# Copyright 2025 Musab Gultekin and the LlamaFactory team.
 #
 # This code is based on the Musab Gultekin's functionary library.
 # https://github.com/MeetKai/functionary/blob/main/functionary/train/packing/monkey_patch_packing.py

src/llamafactory/model/model_utils/quantization.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's Transformers and Optimum library.
 # https://github.com/huggingface/transformers/blob/v4.41.0/src/transformers/utils/quantization_config.py

src/llamafactory/model/model_utils/rope.py

@@ -1,4 +1,4 @@
-# Copyright 2024 LMSYS and the LlamaFactory team.
+# Copyright 2025 LMSYS and the LlamaFactory team.
 # Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
 #
 # This code is inspired by the LMSYS's FastChat library.

src/llamafactory/model/model_utils/visual.py

@@ -15,7 +15,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Sequence
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Optional
 
@@ -180,7 +179,7 @@ def get_forbidden_modules(config: "PretrainedConfig", finetuning_args: "Finetuni
 
 
 def patch_target_modules(
-    model: "PreTrainedModel", finetuning_args: "FinetuningArguments", target_modules: Sequence[str]
+    model: "PreTrainedModel", finetuning_args: "FinetuningArguments", target_modules: list[str]
 ) -> list[str]:
     r"""Freeze vision tower for VLM LoRA tuning."""
     model_type = getattr(model.config, "model_type", None)

src/llamafactory/train/dpo/trainer.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's TRL library.
 # https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/dpo_trainer.py

src/llamafactory/train/dpo/workflow.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's TRL library.
 # https://github.com/huggingface/trl/blob/v0.8.0/examples/scripts/dpo.py

src/llamafactory/train/kto/trainer.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's TRL library.
 # https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/kto_trainer.py

src/llamafactory/train/kto/workflow.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's TRL library.
 # https://github.com/huggingface/trl/blob/v0.8.0/examples/scripts/kto.py

src/llamafactory/train/ppo/trainer.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's TRL library.
 # https://github.com/huggingface/trl/blob/v0.8.0/trl/trainer/ppo_trainer.py

src/llamafactory/train/ppo/workflow.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's TRL library.
 # https://github.com/huggingface/trl/blob/v0.8.0/examples/scripts/ppo.py

src/llamafactory/train/pt/workflow.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py

src/llamafactory/train/rm/trainer.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer.py

src/llamafactory/train/rm/workflow.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py

src/llamafactory/train/sft/metric.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc., THUDM, and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc., THUDM, and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library and the THUDM's ChatGLM implementation.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py
@@ -37,11 +37,11 @@ if is_jieba_available():
 
 
 if is_nltk_available():
-    from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu
+    from nltk.translate.bleu_score import SmoothingFunction, sentence_bleu  # type: ignore
 
 
 if is_rouge_available():
-    from rouge_chinese import Rouge
+    from rouge_chinese import Rouge  # type: ignore
 
 
 def eval_logit_processor(logits: "torch.Tensor", labels: "torch.Tensor") -> "torch.Tensor":

src/llamafactory/train/sft/trainer.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer_seq2seq.py

src/llamafactory/train/sft/workflow.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the HuggingFace's transformers library.
 # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py

src/llamafactory/train/test_utils.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from collections.abc import Sequence
 from typing import TYPE_CHECKING, Optional, Union
 
 import torch
@@ -33,7 +32,7 @@ if TYPE_CHECKING:
     from ..data.data_utils import DatasetModule
 
 
-def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_keys: Sequence[str] = []) -> None:
+def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_keys: list[str] = []) -> None:
     state_dict_a = model_a.state_dict()
     state_dict_b = model_b.state_dict()
     assert set(state_dict_a.keys()) == set(state_dict_b.keys())

src/llamafactory/train/trainer_utils.py

@@ -1,4 +1,4 @@
-# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
 #
 # This code is inspired by the original GaLore's implementation: https://github.com/jiaweizzhao/GaLore
 # and the original LoRA+'s implementation: https://github.com/nikhil-ghosh-berkeley/loraplus

tests/check_license.py

@@ -0,0 +1,38 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+from pathlib import Path
+
+
+KEYWORDS = ("Copyright", "2025", "LlamaFactory")
+
+
+def main():
+    path_list = []
+    for check_dir in sys.argv[1:]:
+        path_list.extend(Path(check_dir).glob("**/*.py"))
+
+    for path in path_list:
+        with open(path.absolute(), encoding="utf-8") as f:
+            file_content = f.read().strip().split("\n")
+            if not file_content[0]:
+                continue
+
+            print(f"Check license: {path}")
+            assert all(keyword in file_content[0] for keyword in KEYWORDS), f"File {path} does not contain license."
+
+
+if __name__ == "__main__":
+    main()

tests/data/test_mm_plugin.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import os
-from collections.abc import Sequence
 from typing import TYPE_CHECKING, Any
 
 import pytest
@@ -97,7 +96,7 @@ def _check_plugin(
     plugin: "BasePlugin",
     tokenizer: "PreTrainedTokenizer",
     processor: "ProcessorMixin",
-    expected_mm_messages: Sequence[dict[str, str]] = MM_MESSAGES,
+    expected_mm_messages: list[dict[str, str]] = MM_MESSAGES,
     expected_input_ids: list[int] = INPUT_IDS,
     expected_labels: list[int] = LABELS,
     expected_mm_inputs: dict[str, Any] = {},

tests/data/test_template.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import os
-from collections.abc import Sequence
 from typing import TYPE_CHECKING
 
 import pytest
@@ -41,7 +40,7 @@ MESSAGES = [
 
 
 def _check_tokenization(
-    tokenizer: "PreTrainedTokenizer", batch_input_ids: Sequence[Sequence[int]], batch_text: Sequence[str]
+    tokenizer: "PreTrainedTokenizer", batch_input_ids: list[list[int]], batch_text: list[str]
 ) -> None:
     r"""Check token ids and texts.