| from mmengine.config import read_base | |
| from opencompass.models import OpenAI | |
| from opencompass.partitioners import NaivePartitioner | |
| from opencompass.runners import LocalRunner | |
| from opencompass.tasks import OpenICLInferTask | |
| with read_base(): | |
| # choose a list of datasets | |
| from opencompass.configs.datasets.collections.chat_medium import datasets | |
| # and output the results in a choosen format | |
| from opencompass.configs.summarizers.medium import summarizer | |
| api_meta_template = dict(round=[ | |
| dict(role='HUMAN', api_role='HUMAN'), | |
| dict(role='BOT', api_role='BOT', generate=True), | |
| ], ) | |
| models = [ | |
| dict( | |
| abbr='GPT-3.5-turbo-0613', | |
| type=OpenAI, | |
| path='gpt-3.5-turbo-0613', | |
| key= | |
| 'ENV', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well | |
| meta_template=api_meta_template, | |
| query_per_second=1, | |
| max_out_len=2048, | |
| max_seq_len=4096, | |
| batch_size=8), | |
| ] | |
| infer = dict( | |
| partitioner=dict(type=NaivePartitioner), | |
| runner=dict(type=LocalRunner, | |
| max_num_workers=8, | |
| task=dict(type=OpenICLInferTask)), | |
| ) | |