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32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
"""经过审核的模型标识;运行时绝不解析浮动的模型版本。"""
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from dataclasses import asdict, dataclass
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@dataclass(frozen=True)
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class ModelSpec:
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key: str
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name: str
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capability: str
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repository: str
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revision: str
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license: str
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source: str = "huggingface"
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dimensions: int | None = None
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def public(self):
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return asdict(self)
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CATALOG = {
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spec.key: spec for spec in [
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ModelSpec("bekko", "Bekko Embedding v1 A8M", "embedding", "hotchpotch/bekko-embedding-v1-a8m",
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"c721113d59a1d91b447450324f51c4b3332c924a", "MIT", dimensions=384),
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ModelSpec("granite", "Granite Embedding 97M Multilingual r2", "embedding", "ibm-granite/granite-embedding-97m-multilingual-r2",
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"835ad14087e140460703cf0fae09f97d469d65c2", "Apache-2.0", dimensions=384),
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ModelSpec("qwen3-asr", "Qwen3 ASR 0.6B", "transcription", "Qwen/Qwen3-ASR-0.6B",
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"5eb144179a02acc5e5ba31e748d22b0cf3e303b0", "Apache-2.0"),
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ModelSpec("eres2netv2", "ERes2NetV2 中文声纹", "speaker_matching", "iic/speech_eres2netv2_sv_zh-cn_16k-common",
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"3317286545c587ae682dbc166831d9448780eebb", "Apache-2.0", source="modelscope", dimensions=192),
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]
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}
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