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55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
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# This config is used for pass@k evaluation with `num_return_sequences`
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# That model can generate multiple responses for single input
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from mmengine.config import read_base
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from opencompass.partitioners import SizePartitioner
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from opencompass.models import HuggingFaceCausalLM
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from opencompass.runners import LocalRunner
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from opencompass.partitioners import SizePartitioner
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from opencompass.tasks import OpenICLInferTask
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from opencompass.datasets import MBPPDataset_V2, MBPPPassKEvaluator
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with read_base():
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from .datasets.humaneval.humaneval_gen_8e312c import humaneval_datasets
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from .datasets.mbpp.mbpp_gen_1e1056 import mbpp_datasets
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mbpp_datasets[0]['type'] = MBPPDataset_V2
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mbpp_datasets[0]['eval_cfg']['evaluator']['type'] = MBPPPassKEvaluator
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mbpp_datasets[0]['reader_cfg']['output_column'] = 'test_column'
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datasets = []
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datasets += humaneval_datasets
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datasets += mbpp_datasets
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models = [
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dict(
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type=HuggingFaceCausalLM,
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abbr='CodeLlama-7b-Python',
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path="codellama/CodeLlama-7b-Python-hf",
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tokenizer_path='codellama/CodeLlama-7b-Python-hf',
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tokenizer_kwargs=dict(
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padding_side='left',
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truncation_side='left',
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trust_remote_code=True,
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),
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max_out_len=1024,
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max_seq_len=2048,
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batch_size=8,
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model_kwargs=dict(trust_remote_code=True, device_map='auto'),
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generation_kwargs=dict(
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num_return_sequences=10,
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do_sample=True,
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top_p=0.95,
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temperature=0.8,
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),
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run_cfg=dict(num_gpus=1, num_procs=1),
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),
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]
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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=300),
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runner=dict(
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type=LocalRunner, max_num_workers=16,
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task=dict(type=OpenICLInferTask)),
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)
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