Repository: open-compass/opencompass
Issue: 将llama-2-7b-chat 模型转换成 turbomind的形式，应该用哪个配置脚本去测试精度 python3 -m lmdeploy.serve.turbomind.deploy llama2 /models/llama-2-7b-chat
Root cause: Documentation Deficiency

### 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

python3 -m lmdeploy.serve.turbomind.deploy llama2 /models/llama-2-7b-chat

生成了workspace 文件
![image](https://github.com/open-compass/opencompass/assets/12838445/167bba51-1658-4d45-af09-cc4b8b3624c6)

应该用哪个配置文件，现在支持吗？


### Reproduces the problem - code/configuration sample

python3 -m lmdeploy.serve.turbomind.deploy llama2 /models/llama-2-7b-chat

### Reproduces the problem - command or script

python3 -m lmdeploy.serve.turbomind.deploy llama2 /models/llama-2-7b-chat

### Reproduces the problem - error message

python3 -m lmdeploy.serve.turbomind.deploy llama2 /models/llama-2-7b-chat

### Other information

_No response_

将
meta_template = dict(
     round=[
         dict(role='HUMAN', begin='
:', end='\n'),
         dict(role='BOT', begin='
:', end='<eoa>\n', generate=True),
     ],
     eos_token_id=103028)

改成
meta_template = dict(
    round=[
        dict(role="HUMAN", api_role="HUMAN"),
        dict(role="BOT", api_role="BOT", generate=True),
    ],
)


from mmengine.config import read_base
from opencompass.models.turbomind import TurboMindModel
from opencompass.models.llama2 import Llama2,Llama2Chat

with read_base():
    # # choose a list of datasets
    from .datasets.mmlu.mmlu_gen_a484b3 import mmlu_datasets
    from .datasets.ceval.ceval_gen_5f30c7 import ceval_datasets
    from .datasets.SuperGLUE_WiC.SuperGLUE_WiC_gen_d06864 import WiC_datasets
    from .datasets.SuperGLUE_WSC.SuperGLUE_WSC_gen_6dc406 import WSC_datasets
    from .datasets.triviaqa.triviaqa_gen_2121ce import triviaqa_datasets
    from .datasets.gsm8k.gsm8k_gen_1d7fe4 import gsm8k_datasets
    from .datasets.humaneval.humaneval_gen_8e312c import humaneval_datasets
    from .datasets.race.race_gen_69ee4f import race_datasets
    from .datasets.crowspairs.crowspairs_gen_381af0 import crowspairs_datasets


datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])




meta_template = dict(
    round=[
        dict(role="HUMAN", api_role="HUMAN"),
        dict(role="BOT", api_role="BOT", generate=True),
    ],
)


models = [
    dict(
        type=TurboMindModel,
        abbr='internlm-llama2-7b-w4a16',
        path="/workspaceLlama4w16a_new",
        max_out_len=100,
        max_seq_len=2048,
        batch_size=16,
        concurrency=16,
        meta_template=meta_template,
        run_cfg=dict(num_gpus=1, num_procs=1),
    )
]




@lin65505578 hi, I followed your instructions and tested some datasets with **llama2-7b-chat** model in facebook format and lmdeploy's turbomind format, but couldn't reproduce results from opencompass website: https://opencompass.org.cn/model-detail/LLaMA-2-7B-Chat
Is it normal? Is there any special setting when bechmarking llama2 using opencompass? 
BR

here's my reults on face book format  (**last column are results from opencompass website**)
```
dataset                                 version    metric            mode    llama-2-7b-chat   from_opencompass website
--------------------------------------  ---------  ----------------  ------  -----------------
--------- 考试 Exam ---------           -          -                 -       -
ceval                                   -          naive_average     gen     27.38     [31.9]
agieval                                 -          naive_average     gen     26.32    [28.5]
mmlu                                    -          naive_average     gen     30.68    [46.2]
GaokaoBench                             -          -                 -       -
ARC-c                                   -          -                 -       -
--------- 语言 Language ---------       -          -                 -       -
WiC                                     -          -                 -       -
summedits                               -          -                 -       -
chid-dev                                -          -                 -       -
afqmc-dev                               -          -                 -       -
bustm-dev                               -          -                 -       -
cluewsc-dev                             -          -                 -       -
WSC                                     -          -                 -       -
winogrande                              -          -                 -       -
flores_100                              -          -                 -       -
--------- 知识 Knowledge ---------      -          -                 -       -
BoolQ                                   -          -                 -       -
commonsense_qa                          -          -                 -       -
nq                                      -          -                 -       -
triviaqa                                2121ce     score             gen     42.62      [46.4]
--------- 推理 Reasoning ---------      -          -                 -       -
cmnli                                   -          -                 -       -
ocnli                                   -          -                 -       -
ocnli_fc-dev                            -          -                 -       -
AX_b                                    -          -                 -       -
AX_g                                    -          -                 -       -
CB                                      -          -                 -       -
RTE                                     -          -                 -       -
story_cloze                             -          -                 -       -
COPA                                    -          -                 -       -
ReCoRD                                  -          -                 -       -
hellaswag                               -          -                 -       -
piqa                                    -          -                 -       -
siqa                                    -          -                 -       -
strategyqa                              -          -                 -       -
math                                    -          -                 -       -
gsm8k                                   1d7fe4     accuracy          gen     28.89    [26.3]
TheoremQA                               -          -                 -       -
openai_humaneval                        8e312c     humaneval_pass@1  gen     5.49     [12.2]
mbpp                                    -          -                 -       -
bbh                                     -          -                 -       -
--------- 理解 Understanding ---------  -          -                 -       -
C3                                      -          -                 -       -
CMRC_dev                                -          -                 -       -
DRCD_dev                                -          -                 -       -
MultiRC                                 -          -                 -       -
race-middle                             -          -                 -       -
race-high                               -          -                 -       -
openbookqa_fact                         -          -                 -       -
csl_dev                                 -          -                 -       -
lcsts                                   -          -                 -       -
Xsum                                    -          -                 -       -
eprstmt-dev                             -          -                 -       -
lambada                                 -          -                 -       -
tnews-dev                               -          -                 -       -
```

here's my reults on lmdeploy's turbomind format
```
dataset                                 version    metric            mode    llama2-chat-7b-turbomind
--------------------------------------  ---------  ----------------  ------  --------------------------
--------- 考试 Exam ---------           -          -                 -       -
ceval                                   -          naive_average     gen     28.24
agieval                                 -          naive_average     gen     26.72
mmlu                                    -          naive_average     gen     35.41
GaokaoBench                             -          -                 -       -
ARC-c                                   -          -                 -       -
--------- 语言 Language ---------       -          -                 -       -
WiC                                     -          -                 -       -
summedits                               -          -                 -       -
chid-dev                                -          -                 -       -
afqmc-dev                               -          -                 -       -
bustm-dev                               -          -                 -       -
cluewsc-dev                             -          -                 -       -
WSC                                     -          -                 -       -
winogrande                              -          -                 -       -
flores_100                              -          -                 -       -
--------- 知识 Knowledge ---------      -          -                 -       -
BoolQ                                   -          -                 -       -
commonsense_qa                          -          -                 -       -
nq                                      -          -                 -       -
triviaqa                                2121ce     score             gen     42.83
--------- 推理 Reasoning ---------      -          -                 -       -
cmnli                                   -          -                 -       -
ocnli                                   -          -                 -       -
ocnli_fc-dev                            -          -                 -       -
AX_b                                    -          -                 -       -
AX_g                                    -          -                 -       -
CB                                      -          -                 -       -
RTE                                     -          -                 -       -
story_cloze                             -          -                 -       -
COPA                                    -          -                 -       -
ReCoRD                                  -          -                 -       -
hellaswag                               -          -                 -       -
piqa                                    -          -                 -       -
siqa                                    -          -                 -       -
strategyqa                              -          -                 -       -
math                                    -          -                 -       -
gsm8k                                   1d7fe4     accuracy          gen     26.46
TheoremQA                               -          -                 -       -
openai_humaneval                        8e312c     humaneval_pass@1  gen     6.71
mbpp                                    -          -                 -       -
bbh                                     -          -                 -       -
--------- 理解 Understanding ---------  -          -                 -       -
C3                                      -          -                 -       -
CMRC_dev                                -          -                 -       -
DRCD_dev                                -          -                 -       -
MultiRC                                 -          -                 -       -
race-middle                             -          -                 -       -
race-high                               -          -                 -       -
openbookqa_fact                         -          -                 -       -
csl_dev                                 -          -                 -       -
lcsts                                   -          -                 -       -
Xsum                                    -          -                 -       -
eprstmt-dev                             -          -                 -       -
lambada                                 -          -                 -       -
tnews-dev                               -          -                 -       -
```


hi we have supported vllm and lmdeploy by a simple way, you just need to set 
```
--accelerator lmdeploy
```
Feel free to reopen it if needed