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Move top_p and top_k higher up in the UI and CLI help

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oobabooga 2026-03-15 09:34:17 -07:00
parent 80d0c03bab
commit bfea49b197
3 changed files with 8 additions and 8 deletions
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4
modules/presets.py
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@ -16,9 +16,10 @@ default_preset_values = {
'dynatemp_exponent': 1,
'smoothing_factor': 0,
'smoothing_curve': 1,
'min_p': 0,
'top_p': 1,
'top_k': 0,
'min_p': 0,
'top_n_sigma': 0,
'typical_p': 1,
'xtc_threshold': 0.1,
'xtc_probability': 0,
@ -26,7 +27,6 @@ default_preset_values = {
'eta_cutoff': 0,
'tfs': 1,
'top_a': 0,
'top_n_sigma': 0,
'adaptive_target': 0,
'adaptive_decay': 0.9,
'dry_multiplier': 0,

8
modules/shared.py
View file

@ -175,9 +175,10 @@ group.add_argument('--dynatemp-high', type=float, default=_d['dynatemp_high'], m
group.add_argument('--dynatemp-exponent', type=float, default=_d['dynatemp_exponent'], metavar='N', help='Dynamic temperature exponent')
group.add_argument('--smoothing-factor', type=float, default=_d['smoothing_factor'], metavar='N', help='Smoothing factor')
group.add_argument('--smoothing-curve', type=float, default=_d['smoothing_curve'], metavar='N', help='Smoothing curve')
group.add_argument('--min-p', type=float, default=_d['min_p'], metavar='N', help='Min P')
group.add_argument('--top-p', type=float, default=_d['top_p'], metavar='N', help='Top P')
group.add_argument('--top-k', type=int, default=_d['top_k'], metavar='N', help='Top K')
group.add_argument('--min-p', type=float, default=_d['min_p'], metavar='N', help='Min P')
group.add_argument('--top-n-sigma', type=float, default=_d['top_n_sigma'], metavar='N', help='Top N Sigma')
group.add_argument('--typical-p', type=float, default=_d['typical_p'], metavar='N', help='Typical P')
group.add_argument('--xtc-threshold', type=float, default=_d['xtc_threshold'], metavar='N', help='XTC threshold')
group.add_argument('--xtc-probability', type=float, default=_d['xtc_probability'], metavar='N', help='XTC probability')
@ -185,7 +186,6 @@ group.add_argument('--epsilon-cutoff', type=float, default=_d['epsilon_cutoff'],
group.add_argument('--eta-cutoff', type=float, default=_d['eta_cutoff'], metavar='N', help='Eta cutoff')
group.add_argument('--tfs', type=float, default=_d['tfs'], metavar='N', help='TFS')
group.add_argument('--top-a', type=float, default=_d['top_a'], metavar='N', help='Top A')
group.add_argument('--top-n-sigma', type=float, default=_d['top_n_sigma'], metavar='N', help='Top N Sigma')
group.add_argument('--adaptive-target', type=float, default=_d['adaptive_target'], metavar='N', help='Adaptive target')
group.add_argument('--adaptive-decay', type=float, default=_d['adaptive_decay'], metavar='N', help='Adaptive decay')
group.add_argument('--dry-multiplier', type=float, default=_d['dry_multiplier'], metavar='N', help='DRY multiplier')
@ -292,9 +292,10 @@ settings = {
'smoothing_curve': neutral_samplers['smoothing_curve'],
# Generation parameters - Curve cutoff
'min_p': neutral_samplers['min_p'],
'top_p': 0.95,
'top_k': neutral_samplers['top_k'],
'min_p': neutral_samplers['min_p'],
'top_n_sigma': neutral_samplers['top_n_sigma'],
'typical_p': neutral_samplers['typical_p'],
'xtc_threshold': neutral_samplers['xtc_threshold'],
'xtc_probability': neutral_samplers['xtc_probability'],
@ -302,7 +303,6 @@ settings = {
'eta_cutoff': neutral_samplers['eta_cutoff'],
'tfs': neutral_samplers['tfs'],
'top_a': neutral_samplers['top_a'],
'top_n_sigma': neutral_samplers['top_n_sigma'],
'adaptive_target': neutral_samplers['adaptive_target'],
'adaptive_decay': neutral_samplers['adaptive_decay'],

4
modules/ui_parameters.py
View file

@ -37,10 +37,10 @@ def create_ui():
shared.gradio['dynamic_temperature'] = gr.Checkbox(value=shared.settings['dynamic_temperature'], label='dynamic_temperature')
gr.Markdown('## Curve cutoff')
shared.gradio['min_p'] = gr.Slider(0.0, 1.0, value=shared.settings['min_p'], step=0.01, label='min_p')
shared.gradio['top_n_sigma'] = gr.Slider(0.0, 5.0, value=shared.settings['top_n_sigma'], step=0.01, label='top_n_sigma')
shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=shared.settings['top_p'], step=0.01, label='top_p')
shared.gradio['top_k'] = gr.Slider(0, 200, value=shared.settings['top_k'], step=1, label='top_k')
shared.gradio['min_p'] = gr.Slider(0.0, 1.0, value=shared.settings['min_p'], step=0.01, label='min_p')
shared.gradio['top_n_sigma'] = gr.Slider(0.0, 5.0, value=shared.settings['top_n_sigma'], step=0.01, label='top_n_sigma')
shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=shared.settings['typical_p'], step=0.01, label='typical_p')
shared.gradio['xtc_threshold'] = gr.Slider(0, 0.5, value=shared.settings['xtc_threshold'], step=0.01, label='xtc_threshold', info='If 2 or more tokens have probability above this threshold, consider removing all but the last one.')
shared.gradio['xtc_probability'] = gr.Slider(0, 1, value=shared.settings['xtc_probability'], step=0.01, label='xtc_probability', info='Probability that the removal will actually happen. 0 disables the sampler. 1 makes it always happen.')