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82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
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from mmengine.config import read_base
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with read_base():
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from .models.qwen.hf_qwen_7b_chat import models as hf_qwen_7b_chat
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from .models.qwen.hf_qwen_14b_chat import models as hf_qwen_14b_chat
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from .models.chatglm.hf_chatglm3_6b import models as hf_chatglm3_6b
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from .models.baichuan.hf_baichuan2_7b_chat import models as hf_baichuan2_7b
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from .models.hf_internlm.hf_internlm_chat_7b import models as hf_internlm_chat_7b
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from .models.hf_internlm.hf_internlm_chat_20b import models as hf_internlm_chat_20b
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from .datasets.subjective.alpaca_eval.alpacav1_judgeby_gpt4 import subjective_datasets as alpacav1
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from .datasets.subjective.alpaca_eval.alpacav2_judgeby_gpt4 import subjective_datasets as alpacav2
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datasets = [*alpacav2]
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from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3
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from opencompass.models.openai_api import OpenAI, OpenAIAllesAPIN
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from opencompass.partitioners import NaivePartitioner, SizePartitioner
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from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
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from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.runners import SlurmSequentialRunner
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from opencompass.tasks import OpenICLInferTask
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from opencompass.tasks.subjective_eval import SubjectiveEvalTask
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from opencompass.summarizers import AlpacaSummarizer
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models = [*hf_qwen_7b_chat, *hf_chatglm3_6b]
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api_meta_template = dict(
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round=[
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dict(role='HUMAN', api_role='HUMAN'),
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dict(role='BOT', api_role='BOT', generate=True)
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],
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reserved_roles=[
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dict(role='SYSTEM', api_role='SYSTEM'),
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],
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)
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infer = dict(
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partitioner=dict(type=NaivePartitioner),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llmeval',
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quotatype='auto',
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max_num_workers=256,
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task=dict(type=OpenICLInferTask)),
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)
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judge_model = dict(
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abbr='GPT4-Turbo',
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type=OpenAI, path='gpt-4-1106-preview',
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key='', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
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meta_template=api_meta_template,
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query_per_second=1,
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max_out_len=1024,
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max_seq_len=4096,
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batch_size=2,
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retry=20,
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temperature = 0
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)
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eval = dict(
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partitioner=dict(
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type=SubjectiveSizePartitioner,
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max_task_size=1000,
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mode='m2n',
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base_models = [*hf_chatglm3_6b],
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compare_models = [*hf_qwen_7b_chat]
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),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llmeval',
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quotatype='auto',
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max_num_workers=256,
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task=dict(
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type=SubjectiveEvalTask,
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judge_cfg=judge_model
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)),
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)
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work_dir = 'outputs/alpaca/'
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summarizer = dict(
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type=AlpacaSummarizer, judge_type='v2'
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)
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