基础作业

python run.py --datasets ceval_gen --hf-path /root/model/Shanghai_AI_Laboratory/internlm2-chat-7b/ --tokenizer-path /root/model/Shanghai_AI_Laboratory/internlm2-chat-7b/ --tokenizer-kwargs padding_side='left' truncation='left' trust_remote_code=True --model-kwargs trust_remote_code=True device_map='auto' --max-seq-len 2048 --max-out-len 16 --batch-size 4 --num-gpus 1 --debug

python run.py --datasets ceval_gen --hf-path /share/temp/model_repos/internlm2-chat-7b/ --tokenizer-path /share/temp/model_repos/internlm2-chat-7b/ --tokenizer-kwargs padding_side='left' truncation='left' trust_remote_code=True --model-kwargs trust_remote_code=True device_map='auto' --max-seq-len 2048 --max-out-len 16 --batch-size 4 --num-gpus 1 --debug

这次评估不知道为什么没有结果

重新搭了环境，还是没结果，但internlm-chat-7b是有结果的

全部删了重新搭环境，再次评测，出结果了

进阶作业

配置文件为

from mmengine.config import read_base
from opencompass.models.turbomind import TurboMindModel

with read_base():
    # choose a list of datasets
    from .datasets.ceval.ceval_gen_5f30c7 import ceval_datasets


datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])

internlm2_meta_template = dict(
    round=[
        dict(role='HUMAN', begin='<|im_start|>user\n', end='<|im_end|>\n'),
        dict(role='BOT', begin='<|im_start|>assistant\n', end='<|im_end|>\n', generate=True),
    ],
    eos_token_id=92542
)

# config for internlm-chat-7b
internlm2_chat_7b = dict(
    type=TurboMindModel,
    abbr='internlm2-chat-7b-turbomind',
    path='internlm/internlm2-chat-7b',
    engine_config=dict(session_len=2048,
                       max_batch_size=32,
                       rope_scaling_factor=1.0),
    gen_config=dict(top_k=1,
                    top_p=0.8,
                    temperature=1.0,
                    max_new_tokens=100),
    max_out_len=100,
    max_seq_len=2048,
    batch_size=32,
    concurrency=32,
    meta_template=internlm2_meta_template,
    run_cfg=dict(num_gpus=1, num_procs=1),
    end_str='<|im_end|>'
)

models = [internlm2_chat_7b]

然后在命令行输入：

~/opencompass# python run.py configs/eval_internlm2_chat_7b_turbomind.py -w outputs/turbomind/internlm2-chat-7b

开始评估

评估结果

可以看出，lmdeploy部署后的internlm_chat_7b评测结果有明显提升！