[Bug] Qwen3.5 cannot run when using HuggingFacewithChatTemplate

Prerequisite

• I have searched Issues and Discussions but cannot get the expected help.
• The bug has not been fixed in the latest version.

Type

I'm evaluating with the officially supported tasks/models/datasets.

Environment

The environment is as follows. The hardware is not correct, you may only check the python environment.

{'CUDA available': False,
 'GCC': 'gcc (Debian 12.2.0-14+deb12u1) 12.2.0',
 'MMEngine': '0.10.7',
 'MUSA available': False,
 'OpenCV': '5.0.0',
 'PyTorch': '2.11.0+cu129',
 'PyTorch compiling details': 'PyTorch built with:\n'
                              '  - GCC 13.3\n'
                              '  - C++ Version: 201703\n'
                              '  - Intel(R) oneAPI Math Kernel Library Version '
                              '2024.2-Product Build 20240605 for Intel(R) 64 '
                              'architecture applications\n'
                              '  - Intel(R) MKL-DNN v3.10.2 (Git Hash '
                              'f1d471933dc852f956fd05389f9313c7148783d5)\n'
                              '  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n'
                              '  - LAPACK is enabled (usually provided by '
                              'MKL)\n'
                              '  - NNPACK is enabled\n'
                              '  - CPU capability usage: AVX512\n'
                              '  - Build settings: BLAS_INFO=mkl, '
                              'BUILD_TYPE=Release, '
                              'COMMIT_SHA=70d99e998b4955e0049d13a98d77ae1b14db1f45, '
                              'CUDA_VERSION=12.9, CUDNN_VERSION=9.17.1, '
                              'CXX_COMPILER=/opt/rh/gcc-toolset-13/root/usr/bin/c++, '
                              'CXX_FLAGS= -fvisibility-inlines-hidden '
                              '-DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO '
                              '-DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON '
                              '-DUSE_FBGEMM -DUSE_MSLK -DUSE_PYTORCH_QNNPACK '
                              '-DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE '
                              '-O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra '
                              '-Werror=return-type -Werror=non-virtual-dtor '
                              '-Werror=range-loop-construct '
                              '-Werror=bool-operation -Wnarrowing '
                              '-Wno-missing-field-initializers '
                              '-Wno-unknown-pragmas -Wno-unused-parameter '
                              '-Wno-strict-overflow -Wno-strict-aliasing '
                              '-Wno-stringop-overflow -Wsuggest-override '
                              '-Wno-psabi -Wno-error=old-style-cast '
                              '-faligned-new -Wno-maybe-uninitialized '
                              '-fno-math-errno -fno-trapping-math '
                              '-Werror=format -Wno-dangling-reference '
                              '-Wno-error=dangling-reference '
                              '-Wno-stringop-overflow, LAPACK_INFO=mkl, '
                              'PERF_WITH_AVX=1, PERF_WITH_AVX2=1, '
                              'TORCH_VERSION=2.11.0, USE_CUDA=ON, '
                              'USE_CUDNN=ON, USE_CUSPARSELT=1, USE_GFLAGS=OFF, '
                              'USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, '
                              'USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, '
                              'USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, '
                              'USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, '
                              'USE_XPU=OFF, \n',
 'Python': '3.12.7 | packaged by Anaconda, Inc. | (main, Oct  4 2024, '
           '13:27:36) [GCC 11.2.0]',
 'TorchVision': '0.26.0+cu129',
 'lmdeploy': "not installed:No module named 'lmdeploy'",
 'numpy_random_seed': np.uint32(2147483648),
 'opencompass': '0.5.3+',
 'sys.platform': 'linux',
 'transformers': '5.12.1'}

Reproduces the problem - code/configuration sample

from opencompass.models import HuggingFacewithChatTemplate
from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask
from opencompass.partitioners import SizePartitioner, NaivePartitioner
from opencompass.runners import LocalRunner
from mmengine.config import read_base
from opencompass.utils.text_postprocessors import extract_non_reasoning_content


with read_base():
    from opencompass.configs.datasets.ceval.ceval_gen import ceval_datasets

datasets = [
    *ceval_datasets
    ]

model_path = "Qwen/Qwen3.5-4B"

model_tag = model_path.split('/')[-1]
work_dir = f'outputs/{model_tag}'
models = [
    dict(
        abbr=model_tag,
        type=HuggingFacewithChatTemplate,
        path=model_path,
        tokenizer_path=model_path,
        max_out_len=8192,
        max_seq_len=32768,
        generation_kwargs=dict(
            temperature=0.7,
            top_p=0.8,
            top_k=20,
            min_p=0.0,
            presence_penalty=1.5,
            repetition_penalty=1.0,
            do_sample=True,
        ),
        model_kwargs=dict(
            device_map='auto',
            trust_remote_code=True,
            torch_dtype='torch.bfloat16',
        ),
        batch_size=8,
        run_cfg=dict(num_gpus=1),
        pred_postprocessor=dict(type=extract_non_reasoning_content)
    )
]

infer = dict(
    partitioner=dict(
        type=SizePartitioner,
        max_task_size=1000,
        strategy='split',
    ),
    runner=dict(
        type=LocalRunner,
        max_num_workers=1,
        task=dict(type=OpenICLInferTask)
    ),
)

eval = dict(
    partitioner=dict(type=NaivePartitioner, n=16),
    runner=dict(type=LocalRunner, max_num_workers=32, task=dict(type=OpenICLEvalTask)))

summarizer = dict(
    dataset_abbrs=[
        'ceval',
        'ceval-hard',
        'ceval-stem',
        'ceval-social-science',
        'ceval-humanities',
        'ceval-other'
    ],
    summary_groups=sum(
        [v for k, v in locals().items() if k.endswith('_summary_groups')], []),
)

Reproduces the problem - command or script

opencompass test.py

Reproduces the problem - error message

Traceback (most recent call last):
  File "/mnt/bn/woker/anaconda3/envs/llm_backup/lib/python3.12/site-packages/opencompass/tasks/openicl_infer.py", line 177, in <module>
    inferencer.run()
  File "/mnt/bn/woker/anaconda3/envs/llm_backup/lib/python3.12/site-packages/opencompass/tasks/openicl_infer.py", line 103, in run
    self._inference()
  File "/mnt/bn/woker/anaconda3/envs/llm_backup/lib/python3.12/site-packages/opencompass/tasks/openicl_infer.py", line 155, in _inference
    inferencer.inference(retriever,
  File "/mnt/bn/woker/anaconda3/envs/llm_backup/lib/python3.12/site-packages/opencompass/openicl/icl_inferencer/icl_gen_inferencer.py", line 177, in inference
    results = self.model.generate_from_template(
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/bn/woker/anaconda3/envs/llm_backup/lib/python3.12/site-packages/opencompass/models/base.py", line 201, in generate_from_template
    return self.generate(inputs, max_out_len=max_out_len, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/bn/woker/anaconda3/envs/llm_backup/lib/python3.12/site-packages/opencompass/models/huggingface_above_v4_33.py", line 445, in generate
    tokens = self.tokenizer.batch_encode_plus(messages, **tokenize_kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/mnt/bn/woker/anaconda3/envs/llm_backup/lib/python3.12/site-packages/transformers/tokenization_utils_base.py", line 1316, in __getattr__
    raise AttributeError(f"{self.__class__.__name__} has no attribute {key}")
AttributeError: Qwen2Tokenizer has no attribute batch_encode_plus. Did you mean: '_encode_plus'?

Other information

I am using Qwen3.5-4B and ceval dataset with HF backend. The code raises AttributeError: Qwen2Tokenizer has no attribute batch_encode_plus. Did you mean: '_encode_plus'?. I find the batch_encode_plus is deprecated after transformers 5.x.x. However, the Qwen3.5 is supported after transformers 5.2.0. I suppose the way to solve this is to change the old API call.