[breaking] bump transformers to 4.45.0 & improve ci (#7746)

* update ci

* fix

* fix

* fix

* fix

* fix

.github/workflows/tests.yml

@@ -31,11 +31,20 @@ jobs:
           - "ubuntu-latest"
           - "windows-latest"
           - "macos-13"
+        transformers:
+          - null
+        include:  # test backward compatibility
+          - python: "3.9"
+            os: "ubuntu-latest"
+            transformers: "4.45.0"
+          - python: "3.9"
+            os: "ubuntu-latest"
+            transformers: "4.49.0"
 
     runs-on: ${{ matrix.os }}
 
     concurrency:
-      group: ${{ github.workflow }}-${{ github.ref }}-${{ matrix.os }}-${{ matrix.python }}
+      group: ${{ github.workflow }}-${{ github.ref }}-${{ matrix.os }}-${{ matrix.python }}-${{ matrix.transformers }}
       cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
 
     env:
@@ -51,19 +60,24 @@ jobs:
         with:
           python-version: ${{ matrix.python }}
           cache: "pip"
-          cache-dependency-path: "setup.py"
+          cache-dependency-path: "**/requirements*.txt"
 
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
           python -m pip install ".[torch,dev]"
 
+      - name: Install transformers
+        if: ${{ matrix.transformers }}
+        run: |
+          python -m pip install "transformers==${{ matrix.transformers }}"
+
       - name: Cache files
         id: hf-hub-cache
         uses: actions/cache@v4
         with:
           path: ${{ runner.temp }}/huggingface
-          key: huggingface-${{ matrix.os }}-${{ matrix.python }}-${{ hashFiles('tests/version.txt') }}
+          key: huggingface-${{ matrix.os }}-${{ matrix.python }}-${{ matrix.transformers }}-${{ hashFiles('tests/version.txt') }}
 
       - name: Check quality
         run: |

README.md

@@ -243,11 +243,11 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 | [Gemma 3](https://huggingface.co/google)                          | 1B/4B/12B/27B                    | gemma3/gemma (1B)   |
 | [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM)           | 9B/32B                           | glm4                |
 | [GPT-2](https://huggingface.co/openai-community)                  | 0.1B/0.4B/0.8B/1.5B              | -                   |
-| [Granite 3.0-3.1](https://huggingface.co/ibm-granite)             | 1B/2B/3B/8B                      | granite3            |
+| [Granite 3.0-3.3](https://huggingface.co/ibm-granite)             | 1B/2B/3B/8B                      | granite3            |
 | [Hunyuan](https://huggingface.co/tencent/)                        | 7B                               | hunyuan             |
 | [Index](https://huggingface.co/IndexTeam)                         | 1.9B                             | index               |
 | [InternLM 2-3](https://huggingface.co/internlm)                   | 7B/8B/20B                        | intern2             |
-| [InternVL2_5-3](https://huggingface.co/OpenGVLab/InternVL)        | 1B/2B/4B/8B/9B/14B/26B/38B/78B   | intern_vl           |
+| [InternVL2.5-3](https://huggingface.co/OpenGVLab/InternVL)\*\*    | 1B/2B/4B/8B/9B/14B/26B/38B/78B   | intern_vl           |
 | [Kimi-VL](https://huggingface.co/moonshotai)                      | 16B                              | kimi_vl             |
 | [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -                   |
 | [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2              |
@@ -415,11 +415,11 @@ huggingface-cli login
 | Mandatory    | Minimum | Recommend |
 | ------------ | ------- | --------- |
 | python       | 3.9     | 3.10      |
-| torch        | 1.13.1  | 2.6.0     |
+| torch        | 2.0.0   | 2.6.0     |
-| transformers | 4.41.2  | 4.50.0    |
+| transformers | 4.45.0  | 4.50.0    |
 | datasets     | 2.16.0  | 3.2.0     |
 | accelerate   | 0.34.0  | 1.2.1     |
-| peft         | 0.14.0  | 0.15.0    |
+| peft         | 0.14.0  | 0.15.1    |
 | trl          | 0.8.6   | 0.9.6     |
 
 | Optional     | Minimum | Recommend |
@@ -428,7 +428,7 @@ huggingface-cli login
 | deepspeed    | 0.10.0  | 0.16.4    |
 | bitsandbytes | 0.39.0  | 0.43.1    |
 | vllm         | 0.4.3   | 0.8.2     |
-| flash-attn   | 2.3.0   | 2.7.2     |
+| flash-attn   | 2.5.6   | 2.7.2     |
 
 ### Hardware Requirement
 
@@ -517,6 +517,7 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
 | torch        | 2.1.0   | 2.4.0          |
 | torch-npu    | 2.1.0   | 2.4.0.post2    |
 | deepspeed    | 0.13.2  | 0.13.2         |
+| vllm-ascend  | -       | 0.7.3          |
 
 Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.
 

README_zh.md

@@ -246,11 +246,11 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
 | [Gemma 3](https://huggingface.co/google)                          | 1B/4B/12B/27B                    | gemma3/gemma (1B)   |
 | [GLM-4/GLM-4-0414/GLM-Z1](https://huggingface.co/THUDM)           | 9B/32B                           | glm4                |
 | [GPT-2](https://huggingface.co/openai-community)                  | 0.1B/0.4B/0.8B/1.5B              | -                   |
-| [Granite 3.0-3.1](https://huggingface.co/ibm-granite)             | 1B/2B/3B/8B                      | granite3            |
+| [Granite 3.0-3.3](https://huggingface.co/ibm-granite)             | 1B/2B/3B/8B                      | granite3            |
 | [Hunyuan](https://huggingface.co/tencent/)                        | 7B                               | hunyuan             |
 | [Index](https://huggingface.co/IndexTeam)                         | 1.9B                             | index               |
 | [InternLM 2-3](https://huggingface.co/internlm)                   | 7B/8B/20B                        | intern2             |
-| [InternVL2_5-3](https://huggingface.co/OpenGVLab/InternVL)        | 1B/2B/4B/8B/9B/14B/26B/38B/78B   | intern_vl           |
+| [InternVL2.5-3](https://huggingface.co/OpenGVLab/InternVL)\*\*    | 1B/2B/4B/8B/9B/14B/26B/38B/78B   | intern_vl           |
 | [Kimi-VL](https://huggingface.co/moonshotai)                      | 16B                              | kimi_vl             |
 | [Llama](https://github.com/facebookresearch/llama)                | 7B/13B/33B/65B                   | -                   |
 | [Llama 2](https://huggingface.co/meta-llama)                      | 7B/13B/70B                       | llama2              |
@@ -418,11 +418,11 @@ huggingface-cli login
 | 必需项        | 至少     | 推荐      |
 | ------------ | ------- | --------- |
 | python       | 3.9     | 3.10      |
-| torch        | 1.13.1  | 2.6.0     |
+| torch        | 2.0.0   | 2.6.0     |
-| transformers | 4.41.2  | 4.50.0    |
+| transformers | 4.45.0  | 4.50.0    |
 | datasets     | 2.16.0  | 3.2.0     |
 | accelerate   | 0.34.0  | 1.2.1     |
-| peft         | 0.14.0  | 0.15.0    |
+| peft         | 0.14.0  | 0.15.1    |
 | trl          | 0.8.6   | 0.9.6     |
 
 | 可选项        | 至少     | 推荐      |
@@ -431,7 +431,7 @@ huggingface-cli login
 | deepspeed    | 0.10.0  | 0.16.4    |
 | bitsandbytes | 0.39.0  | 0.43.1    |
 | vllm         | 0.4.3   | 0.8.2     |
-| flash-attn   | 2.3.0   | 2.7.2     |
+| flash-attn   | 2.5.6   | 2.7.2     |
 
 ### 硬件依赖
 
@@ -521,6 +521,7 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
 | torch        | 2.1.0   | 2.4.0          |
 | torch-npu    | 2.1.0   | 2.4.0.post2    |
 | deepspeed    | 0.13.2  | 0.13.2         |
+| vllm-ascend  | -       | 0.7.3          |
 
 请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。
 

requirements.txt

@@ -1,4 +1,4 @@
-transformers>=4.41.2,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
+transformers>=4.45.0,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
 datasets>=2.16.0,<=3.5.0
 accelerate>=0.34.0,<=1.6.0
 peft>=0.14.0,<=0.15.1

scripts/api_example/test_image.py

@@ -23,8 +23,8 @@ require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
 
 def main():
     client = OpenAI(
-        api_key="{}".format(os.environ.get("API_KEY", "0")),
+        api_key="{}".format(os.getenv("API_KEY", "0")),
-        base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
+        base_url="http://localhost:{}/v1".format(os.getenv("API_PORT", 8000)),
     )
     messages = []
     messages.append(

scripts/api_example/test_toolcall.py

@@ -33,8 +33,8 @@ def calculate_gpa(grades: list[str], hours: list[int]) -> float:
 
 def main():
     client = OpenAI(
-        api_key="{}".format(os.environ.get("API_KEY", "0")),
+        api_key="{}".format(os.getenv("API_KEY", "0")),
-        base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
+        base_url="http://localhost:{}/v1".format(os.getenv("API_PORT", 8000)),
     )
     tools = [
         {

src/llamafactory/__init__.py

@@ -18,18 +18,11 @@ Level:
   api, webui > chat, eval, train > data, model > hparams > extras
 
 Dependency graph:
-  main:
+  transformers>=4.41.2,<=4.43.0,!=4.46.*,!=4.47.*,!=4.48.0
-    transformers>=4.41.2,<=4.51.3,!=4.46.*,!=4.47.*,!=4.48.0
+  datasets>=2.16.0,<=3.5.0
-    datasets>=2.16.0,<=3.5.0
+  accelerate>=0.34.0,<=1.6.0
-    accelerate>=0.34.0,<=1.6.0
+  peft>=0.14.0,<=0.15.1
-    peft>=0.14.0,<=0.15.1
+  trl>=0.8.6,<=0.9.6
-    trl>=0.8.6,<=0.9.6
-  attention:
-    transformers>=4.42.4 (gemma+fa2)
-  longlora:
-    transformers>=4.41.2,<4.48.0
-  packing:
-    transformers>=4.43.0
 
 Disable version checking: DISABLE_VERSION_CHECK=1
 Enable VRAM recording: RECORD_VRAM=1

src/llamafactory/chat/hf_engine.py

@@ -25,7 +25,6 @@ from typing_extensions import override
 from ..data import get_template_and_fix_tokenizer
 from ..extras import logging
 from ..extras.constants import AUDIO_PLACEHOLDER, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER, EngineName
-from ..extras.misc import get_logits_processor
 from ..model import load_model, load_tokenizer
 from .base_engine import BaseEngine, Response
 
@@ -178,7 +177,6 @@ class HuggingfaceEngine(BaseEngine):
             inputs=inputs,
             attention_mask=attention_mask,
             generation_config=GenerationConfig(**generating_args),
-            logits_processor=get_logits_processor(),
         )
 
         mm_inputs = template.mm_plugin.get_mm_inputs(**mm_input_dict, batch_ids=[prompt_ids], processor=processor)

src/llamafactory/cli.py

@@ -19,7 +19,6 @@ from copy import deepcopy
 from functools import partial
 
 
-
 USAGE = (
     "-" * 70
     + "\n"

src/llamafactory/data/mm_plugin.py

@@ -25,12 +25,7 @@ from typing import TYPE_CHECKING, BinaryIO, Literal, Optional, TypedDict, Union
 
 import numpy as np
 import torch
-from transformers.image_utils import (
+from transformers.image_utils import get_image_size, to_numpy_array
-    get_image_size,
-    make_batched_videos,
-    make_flat_list_of_images,
-    to_numpy_array,
-)
 from typing_extensions import override
 
 from ..extras.constants import AUDIO_PLACEHOLDER, IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
@@ -62,6 +57,10 @@ if is_transformers_version_greater_than("4.45.0"):
     )
 
 
+if is_transformers_version_greater_than("4.49.0"):
+    from transformers.image_utils import make_batched_videos, make_flat_list_of_images
+
+
 if TYPE_CHECKING:
     from av.stream import Stream
     from numpy.typing import NDArray
@@ -487,61 +486,6 @@ class Gemma3Plugin(BasePlugin):
 
 @dataclass
 class InternVLPlugin(BasePlugin):
-    @override
-    def process_messages(
-        self,
-        messages: list[dict[str, str]],
-        images: list["ImageInput"],
-        videos: list["VideoInput"],
-        audios: list["AudioInput"],
-        processor: Optional["ProcessorMixin"],
-    ) -> list[dict[str, str]]:
-        self._validate_input(processor, images, videos, audios)
-        num_image_tokens = 0
-        num_video_tokens = 0
-        image_seqlen = getattr(processor, "image_seq_length") if self.expand_mm_tokens else 1
-        messages = deepcopy(messages)
-        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
-
-        image_pixel_patch_list = mm_inputs.get("image_num_patches", None)  # pathes of images
-        video_num_patches = mm_inputs.get("video_num_patches", None)  # all patches for frames of videos
-        video_patch_indices = mm_inputs.get("video_patch_indices", None)  # num frames of per video
-
-        for message in messages:
-            content = message["content"]
-            while IMAGE_PLACEHOLDER in content:
-                if num_image_tokens >= len(image_pixel_patch_list):
-                    raise ValueError(f"`len(images)` is less than the number of {IMAGE_PLACEHOLDER} tokens.")
-                content = content.replace(
-                    IMAGE_PLACEHOLDER,
-                    f"<img>{'<IMG_CONTEXT>' * image_seqlen * image_pixel_patch_list[num_image_tokens]}</img>",
-                    1,
-                )
-                num_image_tokens += 1
-            message["content"] = content
-
-            while VIDEO_PLACEHOLDER in content:
-                if num_video_tokens >= len(video_patch_indices):
-                    raise ValueError(f"`len(videos)` is less than the number of {VIDEO_PLACEHOLDER} tokens.")
-                current_patch_index = video_patch_indices[num_video_tokens - 1] if num_video_tokens > 0 else 0
-                end_patch_index = video_patch_indices[num_video_tokens]
-                num_patches = list(video_num_patches[current_patch_index:end_patch_index])
-                video_replaced_prompt = "\n".join(
-                    f"Frame{i + 1}: <img>{'<IMG_CONTEXT>' * image_seqlen * num_patches[i]}</img>"
-                    for i in range(len(num_patches))
-                )
-                content = content.replace(VIDEO_PLACEHOLDER, video_replaced_prompt, 1)
-                num_video_tokens += 1
-            message["content"] = content
-
-        if len(images) != num_image_tokens:
-            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
-
-        if len(videos) != num_video_tokens:
-            raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens.")
-
-        return messages
-
     @override
     def _get_mm_inputs(
         self,
@@ -621,6 +565,63 @@ class InternVLPlugin(BasePlugin):
 
         return mm_inputs
 
+    @override
+    def process_messages(
+        self,
+        messages: list[dict[str, str]],
+        images: list["ImageInput"],
+        videos: list["VideoInput"],
+        audios: list["AudioInput"],
+        processor: Optional["ProcessorMixin"],
+    ) -> list[dict[str, str]]:
+        self._validate_input(processor, images, videos, audios)
+        num_image_tokens = 0
+        num_video_tokens = 0
+        image_seqlen = getattr(processor, "image_seq_length") if self.expand_mm_tokens else 1
+        messages = deepcopy(messages)
+        mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
+
+        image_pixel_patch_list = mm_inputs.get("image_num_patches")  # pathes of images
+        video_num_patches = mm_inputs.get("video_num_patches")  # all patches for frames of videos
+        video_patch_indices = mm_inputs.get("video_patch_indices")  # num frames of per video
+
+        for message in messages:
+            content = message["content"]
+            while IMAGE_PLACEHOLDER in content:
+                if num_image_tokens >= len(image_pixel_patch_list):
+                    raise ValueError(f"`len(images)` is less than the number of {IMAGE_PLACEHOLDER} tokens.")
+
+                content = content.replace(
+                    IMAGE_PLACEHOLDER,
+                    f"<img>{'<IMG_CONTEXT>' * image_seqlen * image_pixel_patch_list[num_image_tokens]}</img>",
+                    1,
+                )
+                num_image_tokens += 1
+
+            while VIDEO_PLACEHOLDER in content:
+                if num_video_tokens >= len(video_patch_indices):
+                    raise ValueError(f"`len(videos)` is less than the number of {VIDEO_PLACEHOLDER} tokens.")
+
+                current_patch_index = video_patch_indices[num_video_tokens - 1] if num_video_tokens > 0 else 0
+                end_patch_index = video_patch_indices[num_video_tokens]
+                num_patches = list(video_num_patches[current_patch_index:end_patch_index])
+                video_replaced_prompt = "\n".join(
+                    f"Frame{i + 1}: <img>{'<IMG_CONTEXT>' * image_seqlen * num_patches[i]}</img>"
+                    for i in range(len(num_patches))
+                )
+                content = content.replace(VIDEO_PLACEHOLDER, video_replaced_prompt, 1)
+                num_video_tokens += 1
+
+            message["content"] = content
+
+        if len(images) != num_image_tokens:
+            raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")
+
+        if len(videos) != num_video_tokens:
+            raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens.")
+
+        return messages
+
     @override
     def get_mm_inputs(
         self,
@@ -634,12 +635,10 @@ class InternVLPlugin(BasePlugin):
         processor: Optional["ProcessorMixin"],
     ) -> dict[str, Union[list[int], "torch.Tensor"]]:
         self._validate_input(processor, images, videos, audios)
-
         mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
         mm_inputs.pop("image_num_patches", None)
         mm_inputs.pop("video_patch_indices", None)
         mm_inputs.pop("video_num_patches", None)
-
         return mm_inputs
 
 

src/llamafactory/data/template.py

@@ -871,6 +871,18 @@ register_template(
 )
 
 
+register_template(
+    name="granite3_vision",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}\n<|assistant|>\n"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}\n"]),
+    default_system=(
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's questions."
+    ),
+    mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
+)
+
+
 register_template(
     name="index",
     format_user=StringFormatter(slots=["reserved_0{{content}}reserved_1"]),

src/llamafactory/extras/constants.py

@@ -22,7 +22,7 @@ from peft.utils import WEIGHTS_NAME as ADAPTER_WEIGHTS_NAME
 from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME
 
 
-AUDIO_PLACEHOLDER = os.environ.get("AUDIO_PLACEHOLDER", "<audio>")
+AUDIO_PLACEHOLDER = os.getenv("AUDIO_PLACEHOLDER", "<audio>")
 
 CHECKPOINT_NAMES = {
     SAFE_ADAPTER_WEIGHTS_NAME,
@@ -50,7 +50,7 @@ FILEEXT2TYPE = {
 
 IGNORE_INDEX = -100
 
-IMAGE_PLACEHOLDER = os.environ.get("IMAGE_PLACEHOLDER", "<image>")
+IMAGE_PLACEHOLDER = os.getenv("IMAGE_PLACEHOLDER", "<image>")
 
 LAYERNORM_NAMES = {"norm", "ln"}
 
@@ -89,7 +89,7 @@ SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
 
 SWANLAB_CONFIG = "swanlab_public_config.json"
 
-VIDEO_PLACEHOLDER = os.environ.get("VIDEO_PLACEHOLDER", "<video>")
+VIDEO_PLACEHOLDER = os.getenv("VIDEO_PLACEHOLDER", "<video>")
 
 V_HEAD_WEIGHTS_NAME = "value_head.bin"
 
@@ -838,11 +838,46 @@ register_model_group(
             DownloadSource.DEFAULT: "ibm-granite/granite-3.1-8b-instruct",
             DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.1-8b-instruct",
         },
+        "Granite-3.2-2B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.2-2b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.2-2b-instruct",
+        },
+        "Granite-3.2-8B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.2-8b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.2-8b-instruct",
+        },
+        "Granite-3.3-2B-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.3-2b-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-2b-base",
+        },
+        "Granite-3.3-8B-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.3-8b-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-8b-base",
+        },
+        "Granite-3.3-2B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.3-2b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-2b-instruct",
+        },
+        "Granite-3.3-8B-Instruct": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-3.3-8b-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-3.3-8b-instruct",
+        },
     },
     template="granite3",
 )
 
 
+register_model_group(
+    models={
+        "Granite-3.2-1B-A400M-Base": {
+            DownloadSource.DEFAULT: "ibm-granite/granite-vision-3.2-2b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/granite-vision-3.2-2b",
+        },
+    },
+    template="granite3_vision",
+)
+
+
 register_model_group(
     models={
         "Hunyuan-7B-Instruct": {
@@ -967,26 +1002,33 @@ register_model_group(
 
 register_model_group(
     models={
-        "InternVL2_5-1B-MPO": {
+        "InternVL2.5-1B-MPO": {
             DownloadSource.DEFAULT: "kingsley01/InternVL2_5-1B-MPO-hf",
+            DownloadSource.MODELSCOPE: "llamafactory/InternVL2_5-1B-MPO-hf",
         },
-        "InternVL2_5-2B-MPO": {
+        "InternVL2.5-2B-MPO": {
             DownloadSource.DEFAULT: "kingsley01/InternVL2_5-2B-MPO-hf",
+            DownloadSource.MODELSCOPE: "llamafactory/InternVL2_5-2B-MPO-hf",
         },
-        "InternVL2_5-4B-MPO": {
+        "InternVL2.5-4B-MPO": {
             DownloadSource.DEFAULT: "kingsley01/InternVL2_5-4B-MPO-hf",
+            DownloadSource.MODELSCOPE: "llamafactory/InternVL2_5-4B-MPO-hf",
         },
-        "InternVL2_5-8B-MPO": {
+        "InternVL2.5-8B-MPO": {
             DownloadSource.DEFAULT: "kingsley01/InternVL2_5-8B-MPO-hf",
+            DownloadSource.MODELSCOPE: "llamafactory/InternVL2_5-8B-MPO-hf",
         },
         "InternVL3-1B-hf": {
             DownloadSource.DEFAULT: "kingsley01/InternVL3-1B-hf",
+            DownloadSource.MODELSCOPE: "llamafactory/InternVL3-1B-hf",
         },
         "InternVL3-2B-hf": {
             DownloadSource.DEFAULT: "kingsley01/InternVL3-2B-hf",
+            DownloadSource.MODELSCOPE: "llamafactory/InternVL3-2B-hf",
         },
         "InternVL3-8B-hf": {
             DownloadSource.DEFAULT: "kingsley01/InternVL3-8B-hf",
+            DownloadSource.MODELSCOPE: "llamafactory/InternVL3-8B-hf",
         },
     },
     template="intern_vl",

src/llamafactory/extras/logging.py

@@ -79,7 +79,7 @@ class _Logger(logging.Logger):
 
 def _get_default_logging_level() -> "logging._Level":
     r"""Return the default logging level."""
-    env_level_str = os.environ.get("LLAMAFACTORY_VERBOSITY", None)
+    env_level_str = os.getenv("LLAMAFACTORY_VERBOSITY", None)
     if env_level_str:
         if env_level_str.upper() in logging._nameToLevel:
             return logging._nameToLevel[env_level_str.upper()]

src/llamafactory/extras/misc.py

@@ -89,7 +89,7 @@ def check_version(requirement: str, mandatory: bool = False) -> None:
 
 def check_dependencies() -> None:
     r"""Check the version of the required packages."""
-    check_version("transformers>=4.41.2,<=4.51.3,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
+    check_version("transformers>=4.43.0,<=4.51.3,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
     check_version("datasets>=2.16.0,<=3.5.0")
     check_version("accelerate>=0.34.0,<=1.6.0")
     check_version("peft>=0.14.0,<=0.15.1")
@@ -141,13 +141,13 @@ def count_parameters(model: "torch.nn.Module") -> tuple[int, int]:
 def get_current_device() -> "torch.device":
     r"""Get the current available device."""
     if is_torch_xpu_available():
-        device = "xpu:{}".format(os.environ.get("LOCAL_RANK", "0"))
+        device = "xpu:{}".format(os.getenv("LOCAL_RANK", "0"))
     elif is_torch_npu_available():
-        device = "npu:{}".format(os.environ.get("LOCAL_RANK", "0"))
+        device = "npu:{}".format(os.getenv("LOCAL_RANK", "0"))
     elif is_torch_mps_available():
-        device = "mps:{}".format(os.environ.get("LOCAL_RANK", "0"))
+        device = "mps:{}".format(os.getenv("LOCAL_RANK", "0"))
     elif is_torch_cuda_available():
-        device = "cuda:{}".format(os.environ.get("LOCAL_RANK", "0"))
+        device = "cuda:{}".format(os.getenv("LOCAL_RANK", "0"))
     else:
         device = "cpu"
 
@@ -155,11 +155,13 @@ def get_current_device() -> "torch.device":
 
 
 def get_device_count() -> int:
-    r"""Get the number of available GPU or NPU devices."""
+    r"""Get the number of available devices."""
     if is_torch_xpu_available():
         return torch.xpu.device_count()
     elif is_torch_npu_available():
         return torch.npu.device_count()
+    elif is_torch_mps_available():
+        return torch.mps.device_count()
     elif is_torch_cuda_available():
         return torch.cuda.device_count()
     else:
@@ -175,10 +177,12 @@ def get_logits_processor() -> "LogitsProcessorList":
 
 def get_peak_memory() -> tuple[int, int]:
     r"""Get the peak memory usage for the current device (in Bytes)."""
-    if is_torch_npu_available():
+    if is_torch_xpu_available():
-        return torch.npu.max_memory_allocated(), torch.npu.max_memory_reserved()
-    elif is_torch_xpu_available():
         return torch.xpu.max_memory_allocated(), torch.xpu.max_memory_reserved()
+    elif is_torch_npu_available():
+        return torch.npu.max_memory_allocated(), torch.npu.max_memory_reserved()
+    elif is_torch_mps_available():
+        return torch.mps.current_allocated_memory(), -1
     elif is_torch_cuda_available():
         return torch.cuda.max_memory_allocated(), torch.cuda.max_memory_reserved()
     else:
@@ -200,9 +204,11 @@ def infer_optim_dtype(model_dtype: "torch.dtype") -> "torch.dtype":
         return torch.float32
 
 
-def is_gpu_or_npu_available() -> bool:
+def is_accelerator_available() -> bool:
-    r"""Check if the GPU or NPU is available."""
+    r"""Check if the accelerator is available."""
-    return is_torch_npu_available() or is_torch_cuda_available() or is_torch_xpu_available()
+    return (
+        is_torch_xpu_available() or is_torch_npu_available() or is_torch_mps_available() or is_torch_cuda_available()
+    )
 
 
 def is_env_enabled(env_var: str, default: str = "0") -> bool:
@@ -229,7 +235,7 @@ def skip_check_imports() -> None:
 
 
 def torch_gc() -> None:
-    r"""Collect GPU or NPU memory."""
+    r"""Collect the device memory."""
     gc.collect()
     if is_torch_xpu_available():
         torch.xpu.empty_cache()
@@ -280,7 +286,7 @@ def use_ray() -> bool:
 
 
 def find_available_port() -> int:
-    """Find an available port on the local machine."""
+    r"""Find an available port on the local machine."""
     sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
     sock.bind(("", 0))
     port = sock.getsockname()[1]
@@ -288,8 +294,8 @@ def find_available_port() -> int:
     return port
 
 
-def fix_proxy(ipv6_enabled: bool) -> None:
+def fix_proxy(ipv6_enabled: bool = False) -> None:
-    """Fix proxy settings for gradio ui."""
+    r"""Fix proxy settings for gradio ui."""
     os.environ["no_proxy"] = "localhost,127.0.0.1,0.0.0.0"
     if ipv6_enabled:
         for name in ("http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"):

src/llamafactory/model/loader.py

@@ -19,7 +19,6 @@ import torch
 from transformers import (
     AutoConfig,
     AutoModelForCausalLM,
-    AutoModelForImageTextToText,
     AutoModelForSeq2SeqLM,
     AutoModelForTextToWaveform,
     AutoModelForVision2Seq,
@@ -30,6 +29,7 @@ from trl import AutoModelForCausalLMWithValueHead
 
 from ..extras import logging
 from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_other_hub
+from ..extras.packages import is_transformers_version_greater_than
 from .adapter import init_adapter
 from .model_utils.liger_kernel import apply_liger_kernel
 from .model_utils.misc import register_autoclass
@@ -39,6 +39,10 @@ from .model_utils.valuehead import load_valuehead_params
 from .patcher import patch_config, patch_model, patch_processor, patch_tokenizer, patch_valuehead_model
 
 
+if is_transformers_version_greater_than("4.46.0"):
+    from transformers import AutoModelForImageTextToText
+
+
 if TYPE_CHECKING:
     from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 
@@ -145,7 +149,10 @@ def load_model(
         else:
             if type(config) in AutoModelForVision2Seq._model_mapping.keys():  # image-text
                 load_class = AutoModelForVision2Seq
-            elif type(config) in AutoModelForImageTextToText._model_mapping.keys():  # image-text
+            elif (
+                is_transformers_version_greater_than("4.46.0")
+                and type(config) in AutoModelForImageTextToText._model_mapping.keys()
+            ):  # image-text
                 load_class = AutoModelForImageTextToText
             elif type(config) in AutoModelForSeq2SeqLM._model_mapping.keys():  # audio-text
                 load_class = AutoModelForSeq2SeqLM

src/llamafactory/model/model_utils/attention.py

@@ -18,7 +18,6 @@ from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_availabl
 
 from ...extras import logging
 from ...extras.constants import AttentionFunction
-from ...extras.misc import check_version
 
 
 if TYPE_CHECKING:
@@ -36,8 +35,6 @@ def configure_attn_implementation(
     if getattr(config, "model_type", None) == "gemma2" and is_trainable:
         if model_args.flash_attn == AttentionFunction.AUTO or model_args.flash_attn == AttentionFunction.FA2:
             if is_flash_attn_2_available():
-                check_version("transformers>=4.42.4")
-                check_version("flash_attn>=2.6.3")
                 if model_args.flash_attn != AttentionFunction.FA2:
                     logger.warning_rank0("Gemma 2 should use flash attention 2, change `flash_attn` to fa2.")
                     model_args.flash_attn = AttentionFunction.FA2

src/llamafactory/model/model_utils/longlora.py

@@ -350,7 +350,7 @@ def llama_sdpa_attention_forward(
 
 
 def _apply_llama_patch() -> None:
-    check_version("transformers>=4.41.2,<4.48.0")
+    check_version("transformers>=4.43.0,<4.48.0", mandatory=True)
     LlamaAttention.forward = llama_attention_forward
     LlamaFlashAttention2.forward = llama_flash_attention_2_forward
     LlamaSdpaAttention.forward = llama_sdpa_attention_forward

src/llamafactory/model/model_utils/packing.py

@@ -43,7 +43,6 @@ import torch
 import torch.nn.functional as F
 
 from ...extras import logging
-from ...extras.misc import check_version
 from ...extras.packages import is_transformers_version_greater_than
 
 
@@ -117,6 +116,5 @@ def configure_packing(model_args: "ModelArguments", is_trainable: bool) -> None:
     if not is_trainable or not model_args.block_diag_attn:
         return
 
-    check_version("transformers>=4.43.0")
     transformers.modeling_flash_attention_utils._get_unpad_data = get_unpad_data
     logger.info_rank0("Using block diagonal attention for sequence packing without cross-attention.")

src/llamafactory/train/callbacks.py

@@ -188,7 +188,7 @@ class LogCallback(TrainerCallback):
         self.webui_mode = is_env_enabled("LLAMABOARD_ENABLED")
         if self.webui_mode and not use_ray():
             signal.signal(signal.SIGABRT, self._set_abort)
-            self.logger_handler = logging.LoggerHandler(os.environ.get("LLAMABOARD_WORKDIR"))
+            self.logger_handler = logging.LoggerHandler(os.getenv("LLAMABOARD_WORKDIR"))
             logging.add_handler(self.logger_handler)
             transformers.logging.add_handler(self.logger_handler)
 

src/llamafactory/train/sft/workflow.py

@@ -20,7 +20,7 @@ from typing import TYPE_CHECKING, Optional
 from ...data import SFTDataCollatorWith4DAttentionMask, get_dataset, get_template_and_fix_tokenizer
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
-from ...extras.misc import calculate_tps, get_logits_processor
+from ...extras.misc import calculate_tps
 from ...extras.ploting import plot_loss
 from ...model import load_model, load_tokenizer
 from ..trainer_utils import create_modelcard_and_push
@@ -82,7 +82,6 @@ def run_sft(
     gen_kwargs = generating_args.to_dict(obey_generation_config=True)
     gen_kwargs["eos_token_id"] = [tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids
     gen_kwargs["pad_token_id"] = tokenizer.pad_token_id
-    gen_kwargs["logits_processor"] = get_logits_processor()
 
     # Initialize our Trainer
     trainer = CustomSeq2SeqTrainer(

src/llamafactory/webui/chatter.py

@@ -77,10 +77,10 @@ class WebChatModel(ChatModel):
         if not lazy_init:  # read arguments from command line
             super().__init__()
 
-        if demo_mode and os.environ.get("DEMO_MODEL") and os.environ.get("DEMO_TEMPLATE"):  # load demo model
+        if demo_mode and os.getenv("DEMO_MODEL") and os.getenv("DEMO_TEMPLATE"):  # load demo model
-            model_name_or_path = os.environ.get("DEMO_MODEL")
+            model_name_or_path = os.getenv("DEMO_MODEL")
-            template = os.environ.get("DEMO_TEMPLATE")
+            template = os.getenv("DEMO_TEMPLATE")
-            infer_backend = os.environ.get("DEMO_BACKEND", "huggingface")
+            infer_backend = os.getenv("DEMO_BACKEND", "huggingface")
             super().__init__(
                 dict(model_name_or_path=model_name_or_path, template=template, infer_backend=infer_backend)
             )

src/llamafactory/webui/runner.py

@@ -23,7 +23,7 @@ from transformers.trainer import TRAINING_ARGS_NAME
 from transformers.utils import is_torch_npu_available
 
 from ..extras.constants import LLAMABOARD_CONFIG, PEFT_METHODS, TRAINING_STAGES
-from ..extras.misc import is_gpu_or_npu_available, torch_gc, use_ray
+from ..extras.misc import is_accelerator_available, torch_gc, use_ray
 from ..extras.packages import is_gradio_available
 from .common import (
     DEFAULT_CACHE_DIR,
@@ -108,7 +108,7 @@ class Runner:
             if not get("eval.output_dir"):
                 return ALERTS["err_no_output_dir"][lang]
 
-        if not from_preview and not is_gpu_or_npu_available():
+        if not from_preview and not is_accelerator_available():
             gr.Warning(ALERTS["warn_no_cuda"][lang])
 
         return ""

tests/data/test_mm_plugin.py

@@ -20,6 +20,7 @@ import torch
 from PIL import Image
 
 from llamafactory.data.mm_plugin import get_mm_plugin
+from llamafactory.extras.packages import is_transformers_version_greater_than
 from llamafactory.hparams import get_infer_args
 from llamafactory.model import load_tokenizer
 
@@ -137,6 +138,7 @@ def test_base_plugin():
 
 
 @pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
+@pytest.mark.skipif(not is_transformers_version_greater_than("4.50.0"), reason="Requires transformers>=4.50.0")
 def test_gemma3_plugin():
     image_seqlen = 256
     tokenizer_module = _load_tokenizer_module(model_name_or_path="google/gemma-3-4b-it")
@@ -157,7 +159,7 @@ def test_gemma3_plugin():
     _check_plugin(**check_inputs)
 
 
-@pytest.mark.xfail(reason="cache failure.")
+@pytest.mark.xfail(reason="Unknown error.")
 def test_internvl_plugin():
     image_seqlen = 256
     tokenizer_module = _load_tokenizer_module(model_name_or_path="kingsley01/InternVL2_5-1B-MPO-hf")
@@ -196,6 +198,7 @@ def test_llama4_plugin():
     _check_plugin(**check_inputs)
 
 
+@pytest.mark.skipif(not is_transformers_version_greater_than("4.47.0"), reason="Requires transformers>=4.47.0")
 def test_llava_plugin():
     image_seqlen = 576
     tokenizer_module = _load_tokenizer_module(model_name_or_path="llava-hf/llava-1.5-7b-hf")
@@ -254,6 +257,7 @@ def test_paligemma_plugin():
     _check_plugin(**check_inputs)
 
 
+@pytest.mark.skipif(not is_transformers_version_greater_than("4.50.0"), reason="Requires transformers>=4.50.0")
 def test_pixtral_plugin():
     image_slice_height, image_slice_width = 2, 2
     tokenizer_module = _load_tokenizer_module(model_name_or_path="mistral-community/pixtral-12b")
@@ -291,6 +295,7 @@ def test_qwen2_vl_plugin():
     _check_plugin(**check_inputs)
 
 
+@pytest.mark.skipif(not is_transformers_version_greater_than("4.47.0"), reason="Requires transformers>=4.47.0")
 def test_video_llava_plugin():
     image_seqlen = 256
     tokenizer_module = _load_tokenizer_module(model_name_or_path="LanguageBind/Video-LLaVA-7B-hf")