mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-12-16 03:40:34 +08:00
@@ -95,12 +95,11 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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with gr.Row():
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with gr.Column():
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resize_vocab = gr.Checkbox()
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packing = gr.Checkbox()
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efficient_packing = gr.Checkbox()
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neat_packing = gr.Checkbox()
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with gr.Column():
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upcast_layernorm = gr.Checkbox()
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resize_vocab = gr.Checkbox()
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use_llama_pro = gr.Checkbox()
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with gr.Column():
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@@ -114,10 +113,9 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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warmup_steps,
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neftune_alpha,
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optim,
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resize_vocab,
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packing,
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efficient_packing,
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upcast_layernorm,
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neat_packing,
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resize_vocab,
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use_llama_pro,
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shift_attn,
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report_to,
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@@ -131,10 +129,9 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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warmup_steps=warmup_steps,
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neftune_alpha=neftune_alpha,
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optim=optim,
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resize_vocab=resize_vocab,
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packing=packing,
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efficient_packing=efficient_packing,
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upcast_layernorm=upcast_layernorm,
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neat_packing=neat_packing,
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resize_vocab=resize_vocab,
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use_llama_pro=use_llama_pro,
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shift_attn=shift_attn,
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report_to=report_to,
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@@ -331,7 +328,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
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)
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dataset.focus(list_datasets, [dataset_dir, training_stage], [dataset], queue=False)
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training_stage.change(change_stage, [training_stage], [dataset, packing, efficient_packing], queue=False)
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training_stage.change(change_stage, [training_stage], [dataset, packing], queue=False)
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reward_model.focus(list_checkpoints, [model_name, finetuning_type], [reward_model], queue=False)
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model_name.change(list_output_dirs, [model_name, finetuning_type, current_time], [output_dir], queue=False)
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finetuning_type.change(list_output_dirs, [model_name, finetuning_type, current_time], [output_dir], queue=False)
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