support loading lora from hub
Former-commit-id: 0b34c962bc3368dca62b18ad6c27a0293c3affa5
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@@ -195,7 +195,8 @@ class FinetuningArguments:
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default="mlp",
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metadata={"help": "Name of trainable modules for Freeze fine-tuning. \
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LLaMA choices: [\"mlp\", \"self_attn\"], \
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BLOOM choices: [\"mlp\", \"self_attention\"]"}
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BLOOM choices: [\"mlp\", \"self_attention\"], \
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Baichuan choices: [\"mlp\", \"self_attn\"]"}
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)
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lora_rank: Optional[int] = field(
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default=8,
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@@ -212,8 +213,9 @@ class FinetuningArguments:
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lora_target: Optional[str] = field(
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default="q_proj,v_proj",
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metadata={"help": "Name(s) of target modules to apply LoRA. Use comma to separate multiple modules. \
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LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"up_proj\", \"gate_proj\", \"down_proj\"], \
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BLOOM choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense\"]"}
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LLaMA choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
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BLOOM choices: [\"query_key_value\", \"self_attention.dense\", \"mlp.dense\"], \
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Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"]"}
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)
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def __post_init__(self):
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