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UI: Make accordion outline styling global

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oobabooga 2026-04-04 20:13:20 -07:00
parent 0c033caf0e
commit dfd8ec9c49
3 changed files with 7 additions and 7 deletions
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4
modules/training.py
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@ -52,7 +52,7 @@ def create_ui():
with gr.Column():
always_override = gr.Checkbox(label='Override Existing Files', value=False, info='If the name is the same, checking will replace the existing file, and unchecking will load and continue from it (the rank must be the same).', elem_classes=['no-background'])
with gr.Accordion(label='Target Modules', open=False, elem_classes='tgw-accordion'):
with gr.Accordion(label='Target Modules', open=False):
gr.Markdown("Selects which modules to target in training. Targeting more modules is closer to a full fine-tune at the cost of increased VRAM and adapter size.")
all_linear = gr.Checkbox(label='Target all linear layers', value=True, info='Targets every nn.Linear layer except lm_head. Works for any model architecture. When checked, the individual module checkboxes below are ignored.', elem_classes=['no-background'])
with gr.Row():
@ -87,7 +87,7 @@ def create_ui():
with gr.Row():
lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='cosine', choices=['linear', 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'polynomial', 'inverse_sqrt'], info='Learning rate scheduler - defines how the learning rate changes over time. "Constant" means never change, "linear" means to go in a straight line from the learning rate down to 0, cosine follows a curve, etc.', elem_classes=['slim-dropdown'])
with gr.Accordion(label='Advanced Options', open=False, elem_classes='tgw-accordion'):
with gr.Accordion(label='Advanced Options', open=False):
with gr.Row():
with gr.Column():
optimizer = gr.Dropdown(label='Optimizer', value='adamw_torch', choices=['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_torch_xla', 'adamw_apex_fused', 'adafactor', 'adamw_bnb_8bit', 'adamw_anyprecision', 'sgd', 'adagrad'], info='Optimizer algorithm. adamw_torch is the standard choice. adamw_bnb_8bit uses less VRAM. adafactor is memory-efficient for large models.', elem_classes=['slim-dropdown'])