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104 lines
3.0 KiB
Python
104 lines
3.0 KiB
Python
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from opencompass.models import HuggingFaceCausalLM
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from copy import deepcopy
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from opencompass.models import TurboMindModel
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from mmengine.config import read_base
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from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI
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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 ArenaHardSummarizer
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with read_base():
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from .datasets.subjective.arena_hard.arena_hard_scoring import subjective_datasets
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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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)
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_meta_template = dict(
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round=[
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dict(role="HUMAN", begin="<|begin_of_text|>user<|end_header_id|>\n\n", end="<|eot_id|>"),
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dict(role="BOT", begin="<|begin_of_text|>assistant<|end_header_id|>\n\n", end="<|eot_id|>", generate=True),
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],
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)
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models = [
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dict(
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type=HuggingFaceCausalLM,
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abbr="llama-3-8b-instruct-hf",
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path="meta-llama/Meta-Llama-3-8B-Instruct",
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model_kwargs=dict(device_map="auto"),
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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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use_fast=False,
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),
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meta_template=_meta_template,
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max_out_len=4096,
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max_seq_len=2048,
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batch_size=8,
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run_cfg=dict(num_gpus=1, num_procs=1),
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generation_kwargs={"eos_token_id": [128001, 128009]},
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batch_padding=True,
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)
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]
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datasets = [*subjective_datasets]
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work_dir = 'outputs/arena_hard/'
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# -------------Inferen Stage ----------------------------------------
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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=1000000),
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runner=dict(
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type=LocalRunner,
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max_num_workers=32,
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task=dict(type=OpenICLInferTask)),
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)
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judge_models = [dict(
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abbr='GPT4-Turbo',
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type=OpenAI,
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path='gpt-4-1106-preview',
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key='',
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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=10,
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retry=10,
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temperature = 0,
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)]
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## ------------- Evaluation Configuration
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gpt4_0314 = dict(
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abbr='gpt4-0314',
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type=OpenAI,
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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=1000000,
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mode='m2n',
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infer_order='double',
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base_models=[gpt4_0314],
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compare_models=models,
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judge_models=judge_models,
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),
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runner=dict(type=LocalRunner, max_num_workers=16, task=dict(type=SubjectiveEvalTask)),
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given_pred = [{'abbr':'gpt4-0314', 'path':''}]
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)
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summarizer = dict(
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type=ArenaHardSummarizer
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)
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