update Labeled desc
This commit is contained in:
josc146
@@ -22,8 +22,8 @@ export const Configs: FC = observer(() => {
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const {t} = useTranslation();
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const [selectedIndex, setSelectedIndex] = React.useState(commonStore.currentModelConfigIndex);
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const [selectedConfig, setSelectedConfig] = React.useState(commonStore.modelConfigs[selectedIndex]);
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const navigate = useNavigate();
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const port = selectedConfig.apiParameters.apiPort;
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const updateSelectedIndex = (newIndex: number) => {
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setSelectedIndex(newIndex);
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@@ -104,55 +104,72 @@ export const Configs: FC = observer(() => {
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desc={t('Hover your mouse over the text to view a detailed description. Settings marked with * will take effect immediately after being saved.')}
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content={
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<div className="grid grid-cols-1 sm:grid-cols-2 gap-2">
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<Labeled label={t('API Port')} desc={`127.0.0.1:${selectedConfig.apiParameters.apiPort}`} content={
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<NumberInput value={selectedConfig.apiParameters.apiPort} min={1} max={65535} step={1}
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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apiPort: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Max Response Token *')} content={
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<ValuedSlider value={selectedConfig.apiParameters.maxResponseToken} min={100} max={8100} step={400}
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input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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maxResponseToken: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Temperature *')} content={
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<ValuedSlider value={selectedConfig.apiParameters.temperature} min={0} max={2} step={0.1} input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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temperature: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Top_P *')} content={
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<ValuedSlider value={selectedConfig.apiParameters.topP} min={0} max={1} step={0.1} input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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topP: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Presence Penalty *')} content={
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<ValuedSlider value={selectedConfig.apiParameters.presencePenalty} min={-2} max={2} step={0.1} input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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presencePenalty: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Frequency Penalty *')} content={
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<ValuedSlider value={selectedConfig.apiParameters.frequencyPenalty} min={-2} max={2} step={0.1} input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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frequencyPenalty: data.value
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});
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}}/>
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}/>
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<Labeled label={t('API Port')}
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desc={t('Open the following URL with your browser to view the API documentation') + `: http://127.0.0.1:${port}/docs. ` +
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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}` + '\'.'}
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content={
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<NumberInput value={port} min={1} max={65535} step={1}
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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apiPort: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Max Response Token *')}
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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.')}
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content={
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<ValuedSlider value={selectedConfig.apiParameters.maxResponseToken} min={100} max={8100}
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step={400}
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input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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maxResponseToken: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Temperature *')}
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desc={t('Sampling temperature, the higher the stronger the randomness and creativity, while the lower, the more focused and deterministic it will be.')}
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content={
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<ValuedSlider value={selectedConfig.apiParameters.temperature} min={0} max={2} step={0.1}
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input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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temperature: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Top_P *')}
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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.')}
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content={
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<ValuedSlider value={selectedConfig.apiParameters.topP} min={0} max={1} step={0.1} input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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topP: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Presence Penalty *')}
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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.')}
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content={
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<ValuedSlider value={selectedConfig.apiParameters.presencePenalty} min={-2} max={2}
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step={0.1} input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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presencePenalty: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Frequency Penalty *')}
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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.')}
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content={
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<ValuedSlider value={selectedConfig.apiParameters.frequencyPenalty} min={-2} max={2}
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step={0.1} input
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onChange={(e, data) => {
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setSelectedConfigApiParams({
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frequencyPenalty: data.value
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});
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}}/>
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}/>
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</div>
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}
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/>
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@@ -208,38 +225,45 @@ export const Configs: FC = observer(() => {
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<Option>CUDA</Option>
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</Dropdown>
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}/>
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<Labeled label={t('Precision')} content={
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<Dropdown style={{minWidth: 0}} className="grow" value={selectedConfig.modelParameters.precision}
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selectedOptions={[selectedConfig.modelParameters.precision]}
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onOptionSelect={(_, data) => {
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if (data.optionText) {
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setSelectedConfigModelParams({
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precision: data.optionText as Precision
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});
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}
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}}>
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<Option>fp16</Option>
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<Option>int8</Option>
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<Option>fp32</Option>
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</Dropdown>
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}/>
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<Labeled label={t('Stored Layers')} content={
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<ValuedSlider value={selectedConfig.modelParameters.storedLayers} min={0}
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max={selectedConfig.modelParameters.maxStoredLayers} step={1} input
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onChange={(e, data) => {
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setSelectedConfigModelParams({
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storedLayers: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Enable High Precision For Last Layer')} content={
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<Switch checked={selectedConfig.modelParameters.enableHighPrecisionForLastLayer}
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onChange={(e, data) => {
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setSelectedConfigModelParams({
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enableHighPrecisionForLastLayer: data.checked
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});
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}}/>
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}/>
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<Labeled label={t('Precision')}
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desc={t('int8 uses less VRAM, and is faster, but has slightly lower quality. fp16 has higher quality, and fp32 has the best quality.')}
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content={
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<Dropdown style={{minWidth: 0}} className="grow"
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value={selectedConfig.modelParameters.precision}
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selectedOptions={[selectedConfig.modelParameters.precision]}
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onOptionSelect={(_, data) => {
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if (data.optionText) {
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setSelectedConfigModelParams({
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precision: data.optionText as Precision
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});
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}
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}}>
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<Option>fp16</Option>
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<Option>int8</Option>
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<Option>fp32</Option>
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</Dropdown>
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}/>
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<Labeled label={t('Stored Layers')}
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desc={t('Number of the neural network layers loaded into VRAM, the more you load, the faster the speed, but it consumes more VRAM.')}
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content={
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<ValuedSlider value={selectedConfig.modelParameters.storedLayers} min={0}
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max={selectedConfig.modelParameters.maxStoredLayers} step={1} input
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onChange={(e, data) => {
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setSelectedConfigModelParams({
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storedLayers: data.value
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});
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}}/>
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}/>
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<Labeled label={t('Enable High Precision For Last Layer')}
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desc={t('Whether to use CPU to calculate the last output layer of the neural network with FP32 precision to obtain better quality.')}
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content={
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<Switch checked={selectedConfig.modelParameters.enableHighPrecisionForLastLayer}
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onChange={(e, data) => {
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setSelectedConfigModelParams({
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enableHighPrecisionForLastLayer: data.checked
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});
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}}/>
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}/>
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</div>
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}
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/>
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