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update Labeled desc

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josc146 2023-05-20 11:48:32 +08:00
parent 1573c09db0
commit 4f8e35ce62
3 changed files with 117 additions and 84 deletions
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11
frontend/src/_locales/zh-hans/main.json
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@ -75,5 +75,14 @@
"New Version Available": "新版本可用", "New Version Available": "新版本可用",
"Update": "更新", "Update": "更新",
"Please click the button in the top right corner to start the model": "请点击右上角的按钮启动模型", "Please click the button in the top right corner to start the model": "请点击右上角的按钮启动模型",
"Update Error, Please restart this program": "更新出错, 请重启本程序" "Update Error, Please restart this program": "更新出错, 请重启本程序",
"Open the following URL with your browser to view the API documentation": "使用浏览器打开以下地址查看API文档",
"By default, the maximum number of tokens that can be answered in a single response, it can be changed by the user by specifying API parameters.": "默认情况下, 单个回复最多回答的token数目, 用户可以通过自行指定API参数改变这个值",
"Sampling temperature, the higher the stronger the randomness and creativity, while the lower, the more focused and deterministic it will be.": "采样温度, 越大随机性越强, 更具创造力, 越小则越保守稳定",
"Consider the results of the top n% probability mass, 0.1 considers the top 10%, with higher quality but more conservative, 1 considers all results, with lower quality but more diverse.": "考虑前 n% 概率质量的结果, 0.1 考虑前 10%, 质量更高, 但更保守, 1 考虑所有质量结果, 质量降低, 但更多样",
"Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.": "存在惩罚. 正值根据新token在至今的文本中是否出现过, 来对其进行惩罚, 从而增加了模型涉及新话题的可能性",
"Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.": "频率惩罚. 正值根据新token在至今的文本中出现的频率/次数, 来对其进行惩罚, 从而减少模型原封不动地重复相同句子的可能性",
"int8 uses less VRAM, and is faster, but has slightly lower quality. fp16 has higher quality, and fp32 has the best quality.": "int8占用显存更低, 速度更快, 但质量略微下降. fp16质量更好, fp32质量最好",
"Number of the neural network layers loaded into VRAM, the more you load, the faster the speed, but it consumes more VRAM.": "载入显存的神经网络层数, 载入越多, 速度越快, 但显存消耗越大",
"Whether to use CPU to calculate the last output layer of the neural network with FP32 precision to obtain better quality.": "是否使用cpu以fp32精度计算神经网络的最后一层输出层, 以获得更好的质量"
} }

2
frontend/src/components/Labeled.tsx
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@ -3,7 +3,7 @@ import {Label, Tooltip} from '@fluentui/react-components';
import classnames from 'classnames'; import classnames from 'classnames';
export const Labeled: FC<{ export const Labeled: FC<{
label: string; desc?: string, content: ReactElement, flex?: boolean, spaceBetween?: boolean label: string; desc?: string | null, content: ReactElement, flex?: boolean, spaceBetween?: boolean
}> = ({ }> = ({
label, label,
desc, desc,

56
frontend/src/pages/Configs.tsx
View File

@ -22,8 +22,8 @@ export const Configs: FC = observer(() => {
const {t} = useTranslation(); const {t} = useTranslation();
const [selectedIndex, setSelectedIndex] = React.useState(commonStore.currentModelConfigIndex); const [selectedIndex, setSelectedIndex] = React.useState(commonStore.currentModelConfigIndex);
const [selectedConfig, setSelectedConfig] = React.useState(commonStore.modelConfigs[selectedIndex]); const [selectedConfig, setSelectedConfig] = React.useState(commonStore.modelConfigs[selectedIndex]);
const navigate = useNavigate(); const navigate = useNavigate();
const port = selectedConfig.apiParameters.apiPort;
const updateSelectedIndex = (newIndex: number) => { const updateSelectedIndex = (newIndex: number) => {
setSelectedIndex(newIndex); setSelectedIndex(newIndex);
@ -104,16 +104,22 @@ export const Configs: FC = observer(() => {
desc={t('Hover your mouse over the text to view a detailed description. Settings marked with * will take effect immediately after being saved.')} desc={t('Hover your mouse over the text to view a detailed description. Settings marked with * will take effect immediately after being saved.')}
content={ content={
<div className="grid grid-cols-1 sm:grid-cols-2 gap-2"> <div className="grid grid-cols-1 sm:grid-cols-2 gap-2">
<Labeled label={t('API Port')} desc={`127.0.0.1:${selectedConfig.apiParameters.apiPort}`} content={ <Labeled label={t('API Port')}
<NumberInput value={selectedConfig.apiParameters.apiPort} min={1} max={65535} step={1} desc={t('Open the following URL with your browser to view the API documentation') + `: http://127.0.0.1:${port}/docs. ` +
t('This tool’s API is compatible with OpenAI API. It can be used with any ChatGPT tool you like. Go to the settings of some ChatGPT tool, replace the \'https://api.openai.com\' part in the API address with \'') + `http://127.0.0.1:${port}` + '\'.'}
content={
<NumberInput value={port} min={1} max={65535} step={1}
onChange={(e, data) => { onChange={(e, data) => {
setSelectedConfigApiParams({ setSelectedConfigApiParams({
apiPort: data.value apiPort: data.value
}); });
}}/> }}/>
}/> }/>
<Labeled label={t('Max Response Token *')} content={ <Labeled label={t('Max Response Token *')}
<ValuedSlider value={selectedConfig.apiParameters.maxResponseToken} min={100} max={8100} step={400} desc={t('By default, the maximum number of tokens that can be answered in a single response, it can be changed by the user by specifying API parameters.')}
content={
<ValuedSlider value={selectedConfig.apiParameters.maxResponseToken} min={100} max={8100}
step={400}
input input
onChange={(e, data) => { onChange={(e, data) => {
setSelectedConfigApiParams({ setSelectedConfigApiParams({
@ -121,15 +127,20 @@ export const Configs: FC = observer(() => {
}); });
}}/> }}/>
}/> }/>
<Labeled label={t('Temperature *')} content={ <Labeled label={t('Temperature *')}
<ValuedSlider value={selectedConfig.apiParameters.temperature} min={0} max={2} step={0.1} input desc={t('Sampling temperature, the higher the stronger the randomness and creativity, while the lower, the more focused and deterministic it will be.')}
content={
<ValuedSlider value={selectedConfig.apiParameters.temperature} min={0} max={2} step={0.1}
input
onChange={(e, data) => { onChange={(e, data) => {
setSelectedConfigApiParams({ setSelectedConfigApiParams({
temperature: data.value temperature: data.value
}); });
}}/> }}/>
}/> }/>
<Labeled label={t('Top_P *')} content={ <Labeled label={t('Top_P *')}
desc={t('Consider the results of the top n% probability mass, 0.1 considers the top 10%, with higher quality but more conservative, 1 considers all results, with lower quality but more diverse.')}
content={
<ValuedSlider value={selectedConfig.apiParameters.topP} min={0} max={1} step={0.1} input <ValuedSlider value={selectedConfig.apiParameters.topP} min={0} max={1} step={0.1} input
onChange={(e, data) => { onChange={(e, data) => {
setSelectedConfigApiParams({ setSelectedConfigApiParams({
@ -137,16 +148,22 @@ export const Configs: FC = observer(() => {
}); });
}}/> }}/>
}/> }/>
<Labeled label={t('Presence Penalty *')} content={ <Labeled label={t('Presence Penalty *')}
<ValuedSlider value={selectedConfig.apiParameters.presencePenalty} min={-2} max={2} step={0.1} input desc={t('Positive values penalize new tokens based on whether they appear in the text so far, increasing the model\'s likelihood to talk about new topics.')}
content={
<ValuedSlider value={selectedConfig.apiParameters.presencePenalty} min={-2} max={2}
step={0.1} input
onChange={(e, data) => { onChange={(e, data) => {
setSelectedConfigApiParams({ setSelectedConfigApiParams({
presencePenalty: data.value presencePenalty: data.value
}); });
}}/> }}/>
}/> }/>
<Labeled label={t('Frequency Penalty *')} content={ <Labeled label={t('Frequency Penalty *')}
<ValuedSlider value={selectedConfig.apiParameters.frequencyPenalty} min={-2} max={2} step={0.1} input desc={t('Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model\'s likelihood to repeat the same line verbatim.')}
content={
<ValuedSlider value={selectedConfig.apiParameters.frequencyPenalty} min={-2} max={2}
step={0.1} input
onChange={(e, data) => { onChange={(e, data) => {
setSelectedConfigApiParams({ setSelectedConfigApiParams({
frequencyPenalty: data.value frequencyPenalty: data.value
@ -208,8 +225,11 @@ export const Configs: FC = observer(() => {
<Option>CUDA</Option> <Option>CUDA</Option>
</Dropdown> </Dropdown>
}/> }/>
<Labeled label={t('Precision')} content={ <Labeled label={t('Precision')}
<Dropdown style={{minWidth: 0}} className="grow" value={selectedConfig.modelParameters.precision} desc={t('int8 uses less VRAM, and is faster, but has slightly lower quality. fp16 has higher quality, and fp32 has the best quality.')}
content={
<Dropdown style={{minWidth: 0}} className="grow"
value={selectedConfig.modelParameters.precision}
selectedOptions={[selectedConfig.modelParameters.precision]} selectedOptions={[selectedConfig.modelParameters.precision]}
onOptionSelect={(_, data) => { onOptionSelect={(_, data) => {
if (data.optionText) { if (data.optionText) {
@ -223,7 +243,9 @@ export const Configs: FC = observer(() => {
<Option>fp32</Option> <Option>fp32</Option>
</Dropdown> </Dropdown>
}/> }/>
<Labeled label={t('Stored Layers')} content={ <Labeled label={t('Stored Layers')}
desc={t('Number of the neural network layers loaded into VRAM, the more you load, the faster the speed, but it consumes more VRAM.')}
content={
<ValuedSlider value={selectedConfig.modelParameters.storedLayers} min={0} <ValuedSlider value={selectedConfig.modelParameters.storedLayers} min={0}
max={selectedConfig.modelParameters.maxStoredLayers} step={1} input max={selectedConfig.modelParameters.maxStoredLayers} step={1} input
onChange={(e, data) => { onChange={(e, data) => {
@ -232,7 +254,9 @@ export const Configs: FC = observer(() => {
}); });
}}/> }}/>
}/> }/>
<Labeled label={t('Enable High Precision For Last Layer')} content={ <Labeled label={t('Enable High Precision For Last Layer')}
desc={t('Whether to use CPU to calculate the last output layer of the neural network with FP32 precision to obtain better quality.')}
content={
<Switch checked={selectedConfig.modelParameters.enableHighPrecisionForLastLayer} <Switch checked={selectedConfig.modelParameters.enableHighPrecisionForLastLayer}
onChange={(e, data) => { onChange={(e, data) => {
setSelectedConfigModelParams({ setSelectedConfigModelParams({