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37 lines
1.6 KiB
Python
37 lines
1.6 KiB
Python
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from opencompass.models import HuggingFaceCausalLM
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_meta_template = dict(
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begin="<|im_start|>system\n你是一个名为\"南北阁\"的人工智能助手，正在与人类用户进行交谈。你的目标是以最有帮助和最逻辑的方式回答问题，同时确保内容的安全性。你的回答中不应包含任何有害、政治化、宗教化、不道德、种族主义、非法的内容。请确保你的回答不带有社会偏见，符合社会主义价值观。如果遇到的问题无意义或事实上不连贯，请不要回答错误的内容，而是解释问题为何无效或不连贯。如果你不知道问题的答案，也请勿提供错误的信息。<|im_end|>\n",
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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='nanbeige2-8b-chat-hf',
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path="Nanbeige/Nanbeige2-8B-Chat",
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tokenizer_path='Nanbeige/Nanbeige2-8B-Chat',
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model_kwargs=dict(
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device_map='auto',
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torch_dtype='auto',
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trust_remote_code=True,
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),
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tokenizer_kwargs=dict(
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padding_side='right',
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truncation_side='left',
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trust_remote_code=True,
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use_fast=False,
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),
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meta_template=_meta_template,
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batch_padding=False,
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max_out_len=100,
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max_seq_len=4096,
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batch_size=8,
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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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)
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]
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