mirror of
https://github.com/KoboldAI/KoboldAI-Client.git
synced 2025-06-05 21:59:24 +02:00
Redid presets to have a better method for organizing them
Add convert script for making official.presets files from excel files maintained by LightSaveUs
This commit is contained in:
ebolam
54
aiserver.py
54
aiserver.py
@@ -38,6 +38,7 @@ import bisect
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import functools
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import traceback
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from collections.abc import Iterable
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from collections import OrderedDict
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from typing import Any, Callable, TypeVar, Tuple, Union, Dict, Set, List
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import requests
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@@ -2167,21 +2168,44 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
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#Let's load the presets
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with open('settings/preset/official.presets') as f:
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presets = json.load(f)
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to_use = {"Recommended": {"Default": koboldai_vars.default_preset}}
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#Check for 6B in title
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if '6B' in koboldai_vars.model or '6.7B' in koboldai_vars.model or '1.3B' in koboldai_vars.model:
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to_use['Recommended'].update(presets['6B'])
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for key in presets:
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if key != '6B':
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to_use[key] = presets[key]
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elif '13B' in koboldai_vars.model:
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to_use['Recommended'].update(presets['13B'])
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for key in presets:
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if key != '13B':
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to_use[key] = presets[key]
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koboldai_vars.uid_presets = {x['uid']: x for x in presets}
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#We want our data to be a 2 deep dict. Top level is "Recommended", "Same Class", "Model 1", "Model 2", etc
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#Next layer is "Official", "Custom"
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#Then the preset name
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to_use = OrderedDict()
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to_use["Recommended"] = {"Official": [], "Custom": []}
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to_use["Same Class"] = {"Official": [], "Custom": []}
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used_ids = []
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#Build recommended first:
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for preset in presets:
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if preset['Model Type'] == koboldai_vars.model and preset['uid'] not in used_ids:
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if preset['Model Category'] == 'Custom':
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to_use['Recommended']['Custom'].append(preset)
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else:
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for key in presets:
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to_use[key] = presets[key]
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to_use['Recommended']['Official'].append(preset)
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used_ids.append(preset['uid'])
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#Build Same Class
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for preset in presets:
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if preset['Model Size'] == koboldai_vars.model and preset['uid'] not in used_ids:
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if preset['Model Category'] == 'Custom':
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to_use['Recommended']['Custom'].append(preset)
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else:
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to_use['Recommended']['Official'].append(preset)
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used_ids.append(preset['uid'])
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#Build the rest of the stuff
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for preset in presets:
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if preset['uid'] not in used_ids:
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used_ids.append(preset['uid'])
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if preset['Model Type'] not in to_use:
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to_use[preset['Model Type']] = {"Official": [], "Custom": []}
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if preset['Model Category'] == 'Custom':
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to_use[preset['Model Type']]['Custom'].append(preset)
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else:
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to_use[preset['Model Type']]['Official'].append(preset)
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koboldai_vars.presets = to_use
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# Set up Flask routes
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@@ -6150,6 +6174,8 @@ def get_files_folders(starting_folder):
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#==================================================================#
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@socketio.on('var_change')
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def UI_2_var_change(data):
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if 'value' not in data:
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return
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classname = data['ID'].split("_")[0]
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name = data['ID'][len(classname)+1:]
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classname += "_settings"
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60
convert_xls_to_json_presets.py
Normal file
60
convert_xls_to_json_presets.py
Normal file
@@ -0,0 +1,60 @@
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import pandas as pd
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import sys
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output = []
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sheet_mapper = {"KAI-ADAPTED 13B": "13B", "KAI-ADAPTED 6B": "6B", 'KAI-CUSTOM': 'Custom'}
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for file in ['KoboldAI Settings (6B).xlsx', 'KoboldAI Settings (13B).xlsx', 'KoboldAI Settings (Custom).xlsx', 'KoboldAI Settings (Original).xlsx']:
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presets = pd.read_excel("preset Files/{}".format(file), None)
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for sheet in presets:
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df = presets[sheet]
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if sheet in sheet_mapper:
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sheet = sheet_mapper[sheet]
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df = df.dropna(axis=1, how='all')
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df = df.rename(columns={"Unnamed: 0": "setting"})
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df = pd.melt(df, id_vars=['setting'])
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df = df.rename(columns={"variable": "preset"})
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df['fix'] = df['value'].str.replace(" (KAI)", "", regex=False)
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df.loc[~df['fix'].isnull(), 'value'] = df['fix']
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df = df.drop(columns=['fix'])
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df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("Temp", "5", regex=False)
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df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("K", "0", regex=False)
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df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("TFS", "3", regex=False)
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df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("A", "1", regex=False)
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df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("Typ", "4", regex=False)
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df.loc[df['setting']=='Samplers Order', 'value'] = df['value'].str.replace("P", "2", regex=False)
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settings_mapper = {'Temperature': 'temp', 'Output Length': 'genamt', 'Repetition Penalty': 'rep_pen',
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'Top P': 'top_p', 'Top K': 'top_k', 'Tail-Free': 'tfs', 'Repetition Penalty Range': 'rep_pen_range',
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'Repetition Penalty Slope': 'rep_pen_slope', 'Typical': 'typical', 'Top A': 'top_a',
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'Samplers Order': 'sampler_order', 'Description of settings from the author': 'description',
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'Author': 'Author', 'Model Type': 'Model Type',
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'Description of settings from NovelAI': 'description', 'Model Size': "Model Size"
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}
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df['setting'] = df['setting'].map(settings_mapper)
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try:
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df = df.pivot(index='preset', columns='setting', values='value')
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except:
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print(file)
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display(df)
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raise
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df['Model Type'] = df['Model Type'].str.replace(", ", ",").str.split(",")
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df.loc[:, 'Model Category'] = sheet
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output.append(df)
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#output[sheet] = df.to_json(orient="index")
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df = pd.concat(output)
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df = df.reset_index(drop=False)
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df['uid'] = df.index
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df = df.explode("Model Type")
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df['description'] = df['description'].str.strip()
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with open("official.presets", "w") as f:
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f.write(df.reset_index(drop=True).to_json(orient='records'))
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@@ -109,6 +109,7 @@ class koboldai_vars(object):
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if name[0] == "_":
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super().__setattr__(name, value)
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if name[0] != "_":
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#Send it to the corrent _setting class
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if name in self._model_settings.__dict__:
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setattr(self._model_settings, name, value)
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elif name in self._user_settings.__dict__:
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@@ -241,6 +242,7 @@ class model_settings(settings):
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self.revision = None
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self.presets = [] # Holder for presets
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self.selected_preset = ""
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self.uid_presets = []
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self.default_preset = {}
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#dummy class to eat the tqdm output
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@@ -254,8 +256,22 @@ class model_settings(settings):
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super().__setattr__(name, value)
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#Put variable change actions here
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#set preset values
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if name == 'selected_preset' and value != "":
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if int(value) in self.uid_presets:
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for preset_key, preset_value in self.uid_presets[int(value)].items():
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if preset_key in self.__dict__:
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if type(getattr(self, preset_key)) == int:
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preset_value = int(preset_value)
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elif type(getattr(self, preset_key)) == float:
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preset_value = float(preset_value)
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elif type(getattr(self, preset_key)) == bool:
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preset_value = bool(preset_value)
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elif type(getattr(self, preset_key)) == str:
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preset_value = str(preset_value)
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setattr(self, preset_key, preset_value)
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#Setup TQDP for token generation
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if name == "generated_tkns" and 'tqdm' in self.__dict__:
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elif name == "generated_tkns" and 'tqdm' in self.__dict__:
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if value == 0:
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self.tqdm.reset(total=self.genamt)
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self.tqdm_progress = 0
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File diff suppressed because one or more lines are too long
@@ -325,6 +325,10 @@
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margin-right: 25px;
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}
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.preset_item {
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color: white; /*#2b2a33;*/
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}
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.menubar1, .menubar2, .menubar3 {
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width: 21px;
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height: 3px;
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@@ -291,45 +291,29 @@ function do_presets(data) {
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option.text="presets";
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select.append(option);
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presets = data.value;
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for (const [key, value] of Object.entries(data.value)) {
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var option_group = document.createElement("optgroup");
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option_group.label = key;
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for (const [preset, preset_value] of Object.entries(value)) {
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for (const [group, group_value] of Object.entries(value)) {
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var option = document.createElement("option");
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option.value=key+"|"+preset;
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option.text=preset;
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option.text=group;
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option.disabled = true;
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option.classList.add("preset_group");
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option_group.append(option);
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for (const [preset, preset_value] of Object.entries(group_value)) {
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var option = document.createElement("option");
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option.value=preset;
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option.text=preset_value.preset;
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option.title = preset_value.description;
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option_group.append(option);
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}
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}
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select.append(option_group);
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}
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}
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function selected_preset(data) {
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preset_key = data.value.split("|")[0];
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preset = data.value.split("|")[1];
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if ((data.value == undefined) || (presets[preset_key] == undefined)) {
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return;
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}
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if (presets[preset_key][preset] == undefined) {
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return;
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}
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for (const [key, value] of Object.entries(presets[preset_key][preset])) {
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if (key.charAt(0) != '_') {
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var elements_to_change = document.getElementsByClassName("var_sync_model_"+key);
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for (item of elements_to_change) {
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if (item.tagName.toLowerCase() === 'input') {
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item.value = value;
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} else {
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item.textContent = fix_text(value);
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}
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}
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}
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}
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socket.emit("var_change", {"ID": "model_selected_preset", "value": data.value});
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}
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function update_status_bar(data) {
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var percent_complete = data.value;
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var percent_bar = document.getElementsByClassName("statusbar_inner");
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@@ -378,8 +362,6 @@ function var_changed(data) {
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//Special Case for Presets
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} else if ((data.classname == 'model') && (data.name == 'presets')) {
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do_presets(data);
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} else if ((data.classname == "model") && (data.name == "selected_preset")) {
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selected_preset(data);
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} else if ((data.classname == 'story') && (data.name == 'prompt')) {
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do_prompt(data);
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//Basic Data Syncing
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@@ -1259,7 +1241,7 @@ function sync_to_server(item) {
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//get value
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value = null;
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name = null;
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if ((item.tagName.toLowerCase() === 'input') || (item.tagName.toLowerCase() === 'select') || (item.tagName.toLowerCase() == 'textarea')) {
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if ((item.tagName.toLowerCase() === 'checkbox') || (item.tagName.toLowerCase() === 'input') || (item.tagName.toLowerCase() === 'select') || (item.tagName.toLowerCase() == 'textarea')) {
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if (item.getAttribute("type") == "checkbox") {
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value = item.checked;
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} else {
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@@ -37,7 +37,7 @@
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<div id="Model_Info">
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<div>
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Running Model: <span class="var_sync_model_model">ReadOnly</span>
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<select id=presets class="var_sync_model_selected_preset settings_select" onchange='selected_preset(this)'><option>Preset</option></select>
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<select id=presets class="var_sync_model_selected_preset settings_select" onchange='sync_to_server(this)'><option>Preset</option></select>
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</div>
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<div><button class="btn action_button" onclick="socket.emit('load_model_button', {});">Load Model</button></div>
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</div>
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