Repository: open-compass/opencompass
Issue: [Bug] MBPP score significantly lower than official results
Root cause: Configuration Error

### Prerequisite

- [x] I have searched [Issues](https://github.com/open-compass/opencompass/issues/) and [Discussions](https://github.com/open-compass/opencompass/discussions) but cannot get the expected help.
- [x] The bug has not been fixed in the [latest version](https://github.com/open-compass/opencompass).

### Type

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

### Environment

{'CUDA available': True,
 'GCC': 'gcc (GCC) 7.3.0',
 'MMEngine': '0.10.6',
 'MUSA available': False,
 'OpenCV': '4.11.0',
 'PyTorch': '2.1.0',
 'PyTorch compiling details': 'PyTorch built with:\n'
                              '  - GCC 10.2\n'
                              '  - C++ Version: 201703\n'
                              '  - Intel(R) MKL-DNN v3.1.1 (Git Hash '
                              '64f6bcbcbab628e96f33a62c3e975f8535a7bde4)\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: NO AVX\n'
                              '  - Build settings: BLAS_INFO=open, '
                              'BUILD_TYPE=Release, '
                              'CXX_COMPILER=/opt/rh/devtoolset-10/root/usr/bin/c++, '
                              'CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 '
                              '-fabi-version=11 -fvisibility-inlines-hidden '
                              '-DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO '
                              '-DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER '
                              '-DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK '
                              '-DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE '
                              '-O2 -fPIC -Wall -Wextra -Werror=return-type '
                              '-Werror=non-virtual-dtor -Werror=bool-operation '
                              '-Wnarrowing -Wno-missing-field-initializers '
                              '-Wno-type-limits -Wno-array-bounds '
                              '-Wno-unknown-pragmas -Wno-unused-parameter '
                              '-Wno-unused-function -Wno-unused-result '
                              '-Wno-strict-overflow -Wno-strict-aliasing '
                              '-Wno-stringop-overflow -Wno-psabi '
                              '-Wno-error=pedantic -Wno-error=old-style-cast '
                              '-Wno-invalid-partial-specialization '
                              '-Wno-unused-private-field '
                              '-Wno-aligned-allocation-unavailable '
                              '-Wno-missing-braces -fdiagnostics-color=always '
                              '-faligned-new -Wno-unused-but-set-variable '
                              '-Wno-maybe-uninitialized -fno-math-errno '
                              '-fno-trapping-math -Werror=format '
                              '-Werror=cast-function-type '
                              '-Wno-stringop-overflow, LAPACK_INFO=open, '
                              'TORCH_DISABLE_GPU_ASSERTS=ON, '
                              'TORCH_VERSION=2.1.0, USE_CUDA=OFF, '
                              'USE_CUDNN=OFF, USE_EIGEN_FOR_BLAS=ON, '
                              'USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, '
                              'USE_GLOG=OFF, USE_MKL=OFF, USE_MKLDNN=ON, '
                              'USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, '
                              'USE_OPENMP=ON, USE_ROCM=OFF, \n',
 'Python': '3.10.16 (main, Dec 11 2024, 16:18:56) [GCC 11.2.0]',
 'TorchVision': '0.16.0',
 'lmdeploy': "not installed:No module named 'lmdeploy'",
 'numpy_random_seed': 2147483648,
 'opencompass': '0.3.9+',
 'sys.platform': 'linux',
 'transformers': '4.48.0'}

### Reproduces the problem - code/configuration sample

python run.py --models hf_llama3_1_8b --datasets sanitized_mbpp_gen_742f0c --debug

### Reproduces the problem - command or script

python run.py --models hf_llama3_1_8b --datasets sanitized_mbpp_gen_742f0c --debug

### Reproduces the problem - error message

When I was testing the base model for llama3.1-8b, I found that using the config file in the official readme.md came out with a score of only 43.58, while llama3-8b-turbomind in the official readme.md came out with a score of 54.86, which is an excessive difference. What is the reason for this gap in scores?

![Image](https://github.com/user-attachments/assets/3efc6bd9-0c4d-4a03-b3e4-219d27be007f)

![Image](https://github.com/user-attachments/assets/ba98f151-ded9-4ad5-bf7c-e4a594c8becc)

### Other information

_No response_

the same issue