eval_llama2_chat_vllm.py

from mmengine.config import read_base

with read_base():
    from .datasets.ARC_c.ARC_c_gen_1e0de5 import ARC_c_datasets 
    from .datasets.ARC_e.ARC_e_gen_1e0de5 import ARC_e_datasets
    from .summarizers.example import summarizer

datasets = sum([v for k, v in locals().items() if k.endswith("_datasets") or k == 'datasets'], [])
work_dir = './outputs/llama2-chat/'

from opencompass.models import VLLM


llama2_meta_template = dict(
    round=[
        dict(role="HUMAN", begin='[INST] ', end=' [/INST]'),
        dict(role="BOT", begin=' ', end=' ', generate=True),
    ],
)

models = [
    dict(
        type=VLLM,
        abbr='llama-2-7b-chat-vllm',
        path="Llama-2-7b-chat-hf",
        model_kwargs=dict(tensor_parallel_size=1),
        meta_template=llama2_meta_template,
        max_out_len=100,
        max_seq_len=2048,
        batch_size=1,
        generation_kwargs=dict(temperature=0),
        end_str='[INST]',
        run_cfg=dict(num_gpus=1, num_procs=1),
    )
]