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import functools |
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from pathlib import Path |
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import yaml |
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def load_preset(name): |
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generate_params = { |
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'do_sample': True, |
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'temperature': 1, |
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'top_p': 1, |
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'typical_p': 1, |
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'epsilon_cutoff': 0, |
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'eta_cutoff': 0, |
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'tfs': 1, |
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'top_a': 0, |
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'repetition_penalty': 1, |
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'repetition_penalty_range': 0, |
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'encoder_repetition_penalty': 1, |
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'top_k': 0, |
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'num_beams': 1, |
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'penalty_alpha': 0, |
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'min_length': 0, |
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'length_penalty': 1, |
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'no_repeat_ngram_size': 0, |
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'early_stopping': False, |
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'mirostat_mode': 0, |
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'mirostat_tau': 5.0, |
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'mirostat_eta': 0.1, |
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} |
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if name not in ['None', None, '']: |
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with open(Path(f'presets/{name}.yaml'), 'r') as infile: |
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preset = yaml.safe_load(infile) |
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for k in preset: |
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generate_params[k] = preset[k] |
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generate_params['temperature'] = min(1.99, generate_params['temperature']) |
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return generate_params |
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@functools.cache |
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def load_preset_memoized(name): |
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return load_preset(name) |
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def load_preset_for_ui(name, state): |
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generate_params = load_preset(name) |
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state.update(generate_params) |
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return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']] |
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def generate_preset_yaml(state): |
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data = {k: state[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']} |
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return yaml.dump(data, sort_keys=False) |
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