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125 lines
3.3 KiB
Python
125 lines
3.3 KiB
Python
from mmengine.config import read_base
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from opencompass.models import HuggingFaceCausalLM
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from opencompass.partitioners import NaivePartitioner
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from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
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from opencompass.runners import LocalRunner
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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 FlamesSummarizer
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# -------------Inferen Stage ----------------------------------------
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with read_base():
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from .datasets.flames.flames_gen import flames_datasets
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from .models.hf_internlm.hf_internlm2_chat_7b import models
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datasets = [*flames_datasets]
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from opencompass.models import HuggingFaceCausalLM
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_meta_template = dict(
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round=[
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dict(role='HUMAN', begin='<|im_start|>user\n', end='<|im_end|>\n'),
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dict(role='BOT', begin='<|im_start|>assistant\n', end='<|im_end|>\n', 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='internlm2-chat-7b-hf',
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path="internlm/internlm2-chat-7b",
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tokenizer_path='internlm/internlm2-chat-7b',
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model_kwargs=dict(
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trust_remote_code=True,
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device_map='auto',
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),
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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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trust_remote_code=True,
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),
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max_out_len=2048,
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max_seq_len=2048,
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batch_size=8,
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meta_template=_meta_template,
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run_cfg=dict(num_gpus=1, num_procs=1),
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end_str='<|im_end|>',
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generation_kwargs = {"eos_token_id": [2, 92542], "do_sample": True},
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batch_padding=True,
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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=LocalRunner,
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max_num_workers=256,
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task=dict(type=OpenICLInferTask)),
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)
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# -------------Evalation Stage ----------------------------------------
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## ------------- JudgeLLM Configuration---------------------------------
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internlm1_chat_template = dict(
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round=[
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dict(role='HUMAN', begin='<|User|>:', end='\n'),
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dict(role='BOT', begin='<|Bot|>:', end='<eoa>\n', generate=True),
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],
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)
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judge_models = [
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dict(
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type=HuggingFaceCausalLM,
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abbr='flames-scorer',
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path='CaasiHUANG/flames-scorer',
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tokenizer_path='CaasiHUANG/flames-scorer',
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model_kwargs=dict(
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trust_remote_code=True,
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device_map='auto',
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),
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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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trust_remote_code=True,
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),
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generation_kwargs = {"do_sample": True},
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max_out_len=512,
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max_seq_len=4096,
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batch_size=8,
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meta_template=internlm1_chat_template,
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run_cfg=dict(num_gpus=1, num_procs=1),
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end_str='<eoa>',
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)
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]
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## ------------- Evaluation Configuration----------------
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eval = dict(
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partitioner=dict(
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type=SubjectiveNaivePartitioner,
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mode='singlescore',
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models = models,
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judge_models = judge_models,
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),
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runner=dict(
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type=LocalRunner,
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max_num_workers=256,
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task=dict(
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type=SubjectiveEvalTask
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)),
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)
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summarizer = dict(
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type=FlamesSummarizer, judge_type = 'general'
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)
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work_dir = 'outputs/flames/' |