File size: 1,847 Bytes
0eeee8c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
import functools
from pathlib import Path
import yaml
def default_preset():
return {
'temperature': 1,
'temperature_last': False,
'top_p': 1,
'min_p': 0,
'top_k': 0,
'repetition_penalty': 1,
'presence_penalty': 0,
'frequency_penalty': 0,
'repetition_penalty_range': 0,
'typical_p': 1,
'tfs': 1,
'top_a': 0,
'epsilon_cutoff': 0,
'eta_cutoff': 0,
'guidance_scale': 1,
'penalty_alpha': 0,
'mirostat_mode': 0,
'mirostat_tau': 5,
'mirostat_eta': 0.1,
'do_sample': True,
'encoder_repetition_penalty': 1,
'no_repeat_ngram_size': 0,
'min_length': 0,
'num_beams': 1,
'length_penalty': 1,
'early_stopping': False,
}
def presets_params():
return [k for k in default_preset()]
def load_preset(name):
generate_params = default_preset()
if name not in ['None', None, '']:
with open(Path(f'presets/{name}.yaml'), 'r') as infile:
preset = yaml.safe_load(infile)
for k in preset:
generate_params[k] = preset[k]
generate_params['temperature'] = min(1.99, generate_params['temperature'])
return generate_params
@functools.cache
def load_preset_memoized(name):
return load_preset(name)
def load_preset_for_ui(name, state):
generate_params = load_preset(name)
state.update(generate_params)
return state, *[generate_params[k] for k in presets_params()]
def generate_preset_yaml(state):
defaults = default_preset()
data = {k: state[k] for k in presets_params()}
# Remove entries that are identical to the defaults
for k in list(data.keys()):
if data[k] == defaults[k]:
del data[k]
return yaml.dump(data, sort_keys=False)
|