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New feature: "random preset" button (#4647)
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@ -11,9 +11,13 @@ LLMs work by generating one token at a time. Given your prompt, the model calcul
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### Preset menu
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Can be used to save combinations of parameters for reuse.
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Can be used to save and load combinations of parameters for reuse.
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The built-in presets were not manually chosen. They were obtained after a blind contest called "Preset Arena" where hundreds of people voted. The full results can be found [here](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md).
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* **🎲 button**: creates a random yet interpretable preset. Only 1 parameter of each category is included for the categories: removing tail tokens, avoiding repetition, and flattening the distribution. That is, top_p and top_k are not mixed, and neither are repetition_penalty and frequency_penalty. You can use this button to break out of a loop of bad generations after multiple "Regenerate" attempts.
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#### Built-in presets
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These were obtained after a blind contest called "Preset Arena" where hundreds of people voted. The full results can be found [here](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md).
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A key takeaway is that the best presets are:
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@ -1,8 +1,12 @@
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import functools
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import random
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from pathlib import Path
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import yaml
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from modules import shared
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from modules.loaders import loaders_samplers
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def default_preset():
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return {
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@ -63,6 +67,45 @@ def load_preset_for_ui(name, state):
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return state, *[generate_params[k] for k in presets_params()]
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def random_preset(state):
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params_and_values = {
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'remove_tail_tokens': {
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'top_p': [0.5, 0.8, 0.9, 0.95, 0.99],
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'min_p': [0.5, 0.2, 0.1, 0.05, 0.01],
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'top_k': [3, 5, 10, 20, 30, 40],
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'typical_p': [0.2, 0.575, 0.95],
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'tfs': [0.5, 0.8, 0.9, 0.95, 0.99],
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'top_a': [0.5, 0.2, 0.1, 0.05, 0.01],
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'epsilon_cutoff': [1, 3, 5, 7, 9],
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'eta_cutoff': [3, 6, 9, 12, 15, 18],
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},
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'flatten_distribution': {
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'temperature': [0.5, 0.7, 0.8, 1, 1.2, 1.5, 2.0],
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},
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'repetition': {
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'repetition_penalty': [1, 1.05, 1.1, 1.15, 1.20, 1.25],
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'presence_penalty': [0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0],
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'frequency_penalty': [0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0],
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},
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'other': {
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'temperature_last': [True, False],
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}
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}
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generate_params = default_preset()
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for cat in params_and_values:
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choices = list(params_and_values[cat].keys())
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if shared.args.loader is not None:
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choices = [x for x in choices if x in loaders_samplers[shared.args.loader]]
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if len(choices) > 0:
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choice = random.choice(choices)
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generate_params[choice] = random.choice(params_and_values[cat][choice])
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state.update(generate_params)
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return state, *[generate_params[k] for k in presets_params()]
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def generate_preset_yaml(state):
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defaults = default_preset()
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data = {k: state[k] for k in presets_params()}
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@ -18,6 +18,7 @@ def create_ui(default_preset):
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ui.create_refresh_button(shared.gradio['preset_menu'], lambda: None, lambda: {'choices': utils.get_available_presets()}, 'refresh-button', interactive=not mu)
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shared.gradio['save_preset'] = gr.Button('💾', elem_classes='refresh-button', interactive=not mu)
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shared.gradio['delete_preset'] = gr.Button('🗑️', elem_classes='refresh-button', interactive=not mu)
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shared.gradio['random_preset'] = gr.Button('🎲', elem_classes='refresh-button')
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with gr.Column():
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shared.gradio['filter_by_loader'] = gr.Dropdown(label="Filter by loader", choices=["All"] + list(loaders.loaders_and_params.keys()), value="All", elem_classes='slim-dropdown')
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@ -90,6 +91,7 @@ def create_ui(default_preset):
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def create_event_handlers():
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shared.gradio['filter_by_loader'].change(loaders.blacklist_samplers, gradio('filter_by_loader'), gradio(loaders.list_all_samplers()), show_progress=False)
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shared.gradio['preset_menu'].change(presets.load_preset_for_ui, gradio('preset_menu', 'interface_state'), gradio('interface_state') + gradio(presets.presets_params()))
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shared.gradio['random_preset'].click(presets.random_preset, gradio('interface_state'), gradio('interface_state') + gradio(presets.presets_params()))
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shared.gradio['grammar_file'].change(load_grammar, gradio('grammar_file'), gradio('grammar_string'))
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