forked from p04798526/LLaMA-Factory-Mirror
update args spec
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@ -20,7 +20,8 @@ class FinetuningArguments:
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BLOOM choices: [\"24\", \"30\", \"70\"], \
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Falcon choices: [\"32\", \"60\"], \
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Baichuan choices: [\"32\", \"40\"] \
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Qwen choices: [\"32\"]"}
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Qwen choices: [\"32\"], \
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XVERSE choices: [\"40\"]"}
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)
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num_layer_trainable: Optional[int] = field(
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default=3,
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@ -32,7 +33,8 @@ class FinetuningArguments:
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LLaMA & LLaMA-2 choices: [\"mlp\", \"self_attn\"], \
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BLOOM & Falcon choices: [\"mlp\", \"self_attention\"], \
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Baichuan choices: [\"mlp\", \"self_attn\"], \
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Qwen choices: [\"attn\", \"mlp\"]"}
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Qwen choices: [\"mlp\", \"attn\"], \
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InternLM, XVERSE choices: the same as LLaMA."}
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)
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lora_rank: Optional[int] = field(
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default=8,
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@ -49,10 +51,11 @@ 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 commas to separate multiple modules. \
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LLaMA & LLaMA-2 & InternLM choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
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LLaMA & LLaMA-2 choices: [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
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BLOOM & Falcon 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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Qwen choices: [\"c_attn\", \"c_proj\", \"w1\", \"w2\"]"}
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Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \
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InternLM, XVERSE choices: the same as LLaMA."}
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)
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def __post_init__(self):
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