88 lines
5.7 KiB
JSON
88 lines
5.7 KiB
JSON
{
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"Home": "主页",
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"Train": "训练",
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"About": "关于",
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"Settings": "设置",
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"Go to chat page": "前往聊天页",
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"Manage your configs": "管理你的配置",
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"Manage models": "管理模型",
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"Run": "运行",
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"Offline": "离线",
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"Starting": "启动中",
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"Loading": "读取模型中",
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"Working": "运行中",
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"Stop": "停止",
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"Enable High Precision For Last Layer": "输出层使用高精度",
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"Stored Layers": "载入显存层数",
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"Precision": "精度",
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"Device": "设备",
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"Convert model with these configs": "用这些设置转换模型",
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"Manage Models": "管理模型",
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"Model": "模型",
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"Model Parameters": "模型参数",
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"Frequency Penalty *": "Frequency Penalty *",
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"Presence Penalty *": "Presence Penalty *",
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"Top_P *": "Top_P *",
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"Temperature *": "Temperature *",
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"Max Response Token *": "最大响应 Token *",
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"API Port": "API 端口",
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"Hover your mouse over the text to view a detailed description. Settings marked with * will take effect immediately after being saved.": "把鼠标悬停在文本上查看详细描述. 标记了星号 * 的设置在保存后会立即生效.",
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"Default API Parameters": "默认 API 参数",
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"Provide JSON file URLs for the models manifest. Separate URLs with semicolons. The \"models\" field in JSON files will be parsed into the following table.": "填写模型描述的 JSON 文件地址. 地址间用分号分隔. JSON 文件内的 \"models\" 字段会被分析进下表.",
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"Config Name": "配置名",
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"Refresh": "刷新",
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"Save Config": "保存配置",
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"Model Source Manifest List": "模型源",
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"Models": "模型",
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"Delete Config": "删除配置",
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"Help": "帮助",
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"Version": "版本",
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"New Config": "新建配置",
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"Open Url": "打开网页",
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"Download": "下载",
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"Open Folder": "打开文件夹",
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"Configs": "配置",
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"Automatic Updates Check": "自动检查更新",
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"Updates Check Error": "检查更新失败",
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"Introduction": "介绍",
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"Dark Mode": "深色模式",
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"Language": "语言",
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"In Development": "开发中",
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"Chat": "聊天",
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"Convert": "转换",
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"Actions": "动作",
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"Last updated": "上次更新",
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"Desc": "描述",
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"Size": "文件大小",
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"File": "文件",
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"Config Saved": "配置已保存",
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"Downloading": "正在下载",
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"Loading Model": "正在读取模型",
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"Startup Completed": "启动完成",
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"Failed to switch model": "切换模型失败",
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"Start Converting": "开始转换",
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"Convert Success": "转换成功",
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"Convert Failed": "转换失败",
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"Model Not Found": "模型不存在",
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"Model Status": "模型状态",
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"Clear": "清除",
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"Send": "发送",
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"Type your message here": "在此输入消息",
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"Copy": "复制",
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"Read Aloud": "朗读",
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"Hello! I'm RWKV, an open-source and commercially available large language model.": "你好! 我是RWKV, 一个开源可商用的大语言模型.",
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"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 '": "本工具的API与OpenAI API兼容. 因此可以配合任意你喜欢的ChatGPT工具使用. 打开某个ChatGPT工具的设置, 将API地址中的'https://api.openai.com'部分替换为'",
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"New Version Available": "新版本可用",
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"Update": "更新",
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"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",
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"Update Error, Please restart this program": "更新出错, 请重启本程序",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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",
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"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"
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} |