Repository: open-compass/opencompass
Issue: [Bug] 不支持flores200数据集评测
Root cause: Configuration Error

### 先决条件

- [X] 我已经搜索过 [问题](https://github.com/open-compass/opencompass/issues/) 和 [讨论](https://github.com/open-compass/opencompass/discussions) 但未得到预期的帮助。
- [X] 错误在 [最新版本](https://github.com/open-compass/opencompass) 中尚未被修复。

### 问题类型

我正在使用官方支持的任务/模型/数据集进行评估。

### 环境

{'CUDA available': True,
 'CUDA_HOME': '/usr/local/cuda',
 'GCC': 'gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44)',
 'GPU 0,1,2,3': 'NVIDIA GeForce RTX 3090',
 'MMEngine': '0.10.3',
 'MUSA available': False,
 'NVCC': 'Cuda compilation tools, release 11.0, V11.0.194',
 'OpenCV': '4.9.0',
 'PyTorch': '2.0.0+cu117',
 'PyTorch compiling details': 'PyTorch built with:\n'
                              '  - GCC 9.3\n'
                              '  - C++ Version: 201703\n'
                              '  - Intel(R) oneAPI Math Kernel Library Version '
                              '2021.4-Product Build 20210904 for Intel(R) 64 '
                              'architecture applications\n'
                              '  - Intel(R) MKL-DNN v2.7.3 (Git Hash '
                              '6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\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: AVX2\n'
                              '  - CUDA Runtime 11.7\n'
                              '  - NVCC architecture flags: '
                              '-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n'
                              '  - CuDNN 8.5\n'
                              '  - Magma 2.6.1\n'
                              '  - Build settings: BLAS_INFO=mkl, '
                              'BUILD_TYPE=Release, CUDA_VERSION=11.7, '
                              'CUDNN_VERSION=8.5.0, '
                              'CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, '
                              'CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 '
                              '-fabi-version=11 -Wno-deprecated '
                              '-fvisibility-inlines-hidden -DUSE_PTHREADPOOL '
                              '-DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER '
                              '-DUSE_FBGEMM -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 -Wunused-local-typedefs '
                              '-Wno-unused-parameter -Wno-unused-function '
                              '-Wno-unused-result -Wno-strict-overflow '
                              '-Wno-strict-aliasing '
                              '-Wno-error=deprecated-declarations '
                              '-Wno-stringop-overflow -Wno-psabi '
                              '-Wno-error=pedantic -Wno-error=redundant-decls '
                              '-Wno-error=old-style-cast '
                              '-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=mkl, '
                              'PERF_WITH_AVX=1, PERF_WITH_AVX2=1, '
                              'PERF_WITH_AVX512=1, '
                              'TORCH_DISABLE_GPU_ASSERTS=ON, '
                              'TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, '
                              'USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, '
                              'USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, '
                              'USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, '
                              'USE_OPENMP=ON, USE_ROCM=OFF, \n',
 'Python': '3.8.18 | packaged by conda-forge | (default, Dec 23 2023, '
           '17:21:28) [GCC 12.3.0]',
 'numpy_random_seed': 2147483648,
 'opencompass': '0.2.4+004ed79',
 'sys.platform': 'linux'}

### 重现问题 - 代码/配置示例

python tools/list_configs.py llama3 flores

### 重现问题 - 命令或脚本

查看当前服务器opencompass支持的llama3 flores200的模型和数据集

### 重现问题 - 错误信息


![WX20240424-204913@2x](https://github.com/open-compass/opencompass/assets/144193751/80eb94a3-4a13-4d65-98c2-b40c452d4658)

![WX20240424-204731@2x](https://github.com/open-compass/opencompass/assets/144193751/8185211a-63b6-473a-a71a-a8a2ddf5a7f0)


### 其他信息

opencompass的完整数据集里面有flores200数据集，服务器环境已经配置好，但是使用python tools/list_configs.py llama3 flores无法查看支持的flores200数据集

Please try `flores_gen_806ede`

> 请尝试`flores_gen_806ede`
First of all, thank you for your reply. What are the differences between the following three flores data sets：

flores_gen
lores_gen_806ede
flores_gen_aad4fd






> 请尝试`flores_gen_806ede`

![WX20240424-230540@2x](https://github.com/open-compass/opencompass/assets/144193751/9d0fe96d-956e-495c-90b6-51d450ad67a3)
请问为什么我使用flores200测评，也就是您说的flores_gen_806ede

数据集，为什么在测评的过程中显示的是flores100，如图所示

Apologies for the confusion. We are currently utilizing the `flores200` dataset; however, the configuration `flores_gen_806ede` mistakenly employs the prompt for `flores100`. We will address and rectify this issue promptly.