# SOME DESCRIPTIVE TITLE. # Copyright (C) YEAR THE PACKAGE'S COPYRIGHT HOLDER # This file is distributed under the same license as the PACKAGE package. # FIRST AUTHOR , YEAR. # msgid "" msgstr "" "Project-Id-Version: PACKAGE VERSION\n" "Report-Msgid-Bugs-To: \n" "POT-Creation-Date: 2025-04-29 14:48+0800\n" "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n" "Last-Translator: FULL NAME \n" "Language-Team: LANGUAGE \n" "Language: \n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=UTF-8\n" "Content-Transfer-Encoding: 8bit\n" #: common/auth/authenticate.py:80 msgid "Not logged in, please log in first" msgstr "未登录，请先登录" #: common/auth/authenticate.py:82 common/auth/authenticate.py:89 #: common/auth/authenticate.py:95 msgid "Authentication information is incorrect! illegal user" msgstr "身份验证信息不正确！非法用户" #: common/auth/authentication.py:96 msgid "No permission to access" msgstr "无权限访问" #: common/auth/handle/impl/user_token.py:242 msgid "Login expired" msgstr "登录已过期" #: common/constants/exception_code_constants.py:31 #: users/serializers/login.py:53 msgid "The username or password is incorrect" msgstr "用户名或密码不正确" #: common/constants/exception_code_constants.py:32 msgid "Please log in first and bring the user Token" msgstr "请先登录并携带用户 Token" #: common/constants/exception_code_constants.py:33 #| msgid "Model saving failed" msgid "Email sending failed" msgstr "邮件发送失败" #: common/constants/exception_code_constants.py:34 msgid "Email format error" msgstr "邮箱格式错误" #: common/constants/exception_code_constants.py:35 #| msgid "The user has been disabled, please contact the administrator!" msgid "The email has been registered, please log in directly" msgstr "该邮箱已注册，请直接登录" #: common/constants/exception_code_constants.py:36 #| msgid "The model does not exist, please download the model first" msgid "The email is not registered, please register first" msgstr "该邮箱未注册，请先注册" #: common/constants/exception_code_constants.py:38 msgid "The verification code is incorrect or the verification code has expired" msgstr "验证码不正确或已过期" #: common/constants/exception_code_constants.py:39 #| msgid "The user has been disabled, please contact the administrator!" msgid "The username has been registered, please log in directly" msgstr "用户名已注册，请直接登录" #: common/constants/exception_code_constants.py:41 msgid "" "The username cannot be empty and must be between 6 and 20 characters long." msgstr "用户名不能为空，且长度在6到20个字符之间。" #: common/constants/exception_code_constants.py:43 msgid "Password and confirmation password are inconsistent" msgstr "密码和确认密码不一致" #: common/event/__init__.py:27 msgid "The download process was interrupted, please try again" msgstr "下载过程被中断，请重试" #: common/event/listener_manage.py:90 #, python-brace-format msgid "Query vector data: {paragraph_id_list} error {error} {traceback}" msgstr "查询向量数据：{paragraph_id_list} 错误：{error} {traceback}" #: common/event/listener_manage.py:95 #, python-brace-format msgid "Start--->Embedding paragraph: {paragraph_id_list}" msgstr "开始--->向量段落: {paragraph_id_list}" #: common/event/listener_manage.py:107 #, python-brace-format msgid "Vectorized paragraph: {paragraph_id_list} error {error} {traceback}" msgstr "向量段落: {paragraph_id_list} 错误：{error} {traceback}" #: common/event/listener_manage.py:113 #, python-brace-format msgid "End--->Embedding paragraph: {paragraph_id_list}" msgstr "结束--->向量段落: {paragraph_id_list}" #: common/event/listener_manage.py:122 #, python-brace-format msgid "Start--->Embedding paragraph: {paragraph_id}" msgstr "开始--->向量段落: {paragraph_id}" #: common/event/listener_manage.py:147 #, python-brace-format msgid "Vectorized paragraph: {paragraph_id} error {error} {traceback}" msgstr "向量段落: {paragraph_id} 错误：{error} {traceback}" #: common/event/listener_manage.py:152 #, python-brace-format msgid "End--->Embedding paragraph: {paragraph_id}" msgstr "结束--->向量段落: {paragraph_id}" #: common/event/listener_manage.py:268 #, python-brace-format msgid "Start--->Embedding document: {document_id}" msgstr "开始--->向量文档: {document_id}" #: common/event/listener_manage.py:288 #, python-brace-format msgid "Vectorized document: {document_id} error {error} {traceback}" msgstr "向量文档: {document_id} 错误：{error} {traceback}" #: common/event/listener_manage.py:293 #, python-brace-format msgid "End--->Embedding document: {document_id}" msgstr "结束--->向量文档: {document_id}" #: common/event/listener_manage.py:304 #, python-brace-format msgid "Start--->Embedding knowledge: {knowledge_id}" msgstr "开始--->向量知识库: {knowledge_id}" #: common/event/listener_manage.py:308 #, python-brace-format msgid "Start--->Embedding document: {document_list}" msgstr "开始--->向量文档: {document_list}" #: common/event/listener_manage.py:312 knowledge/task/embedding.py:116 #, python-brace-format msgid "Vectorized knowledge: {knowledge_id} error {error} {traceback}" msgstr "向量知识库: {knowledge_id} 错误：{error} {traceback}" #: common/event/listener_manage.py:315 #, python-brace-format msgid "End--->Embedding knowledge: {knowledge_id}" msgstr "结束--->向量知识库: {knowledge_id}" #: common/exception/handle_exception.py:32 msgid "Unknown exception" msgstr "未知错误" #: common/forms/base_field.py:64 #, python-brace-format msgid "The field {field_label} is required" msgstr "{field_label} 字段是必填项" #: common/forms/slider_field.py:56 #, python-brace-format msgid "The {field_label} cannot be less than {min}" msgstr "{field_label} 不能小于{min}" #: common/forms/slider_field.py:62 #, python-brace-format msgid "The {field_label} cannot be greater than {max}" msgstr "{field_label} 不能大于{max}" #: common/result/api.py:17 common/result/api.py:27 msgid "response code" msgstr "响应码" #: common/result/api.py:18 common/result/api.py:19 common/result/api.py:28 #: common/result/api.py:29 msgid "error prompt" msgstr "错误提示" #: common/result/api.py:43 msgid "total number of data" msgstr "总数据" #: common/result/api.py:44 msgid "current page" msgstr "当前页" #: common/result/api.py:45 msgid "page size" msgstr "每页大小" #: common/result/result.py:31 msgid "Success" msgstr "成功" #: common/utils/common.py:85 msgid "Text-to-speech node, the text content must be of string type" msgstr "文本转语音节点，文本内容必须是字符串类型" #: common/utils/common.py:87 msgid "Text-to-speech node, the text content cannot be empty" msgstr "文本转语音节点，文本内容不能为空" #: common/utils/common.py:239 #, python-brace-format msgid "Limit {count} exceeded, please contact us (https://fit2cloud.com/)." msgstr "超过限制 {count}，请联系我们 (https://fit2cloud.com/)." #: folders/models/folder.py:6 folders/models/folder.py:13 #: folders/serializers/folder.py:86 #| msgid "model name" msgid "folder name" msgstr "文件夹名称" #: folders/models/folder.py:9 folders/models/folder.py:15 #: folders/serializers/folder.py:89 msgid "parent id" msgstr "父级 ID" #: folders/serializers/folder.py:63 msgid "Folder depth cannot exceed 3 levels" msgstr "文件夹深度不能超过3级" #: folders/serializers/folder.py:85 folders/serializers/folder.py:121 #: knowledge/serializers/knowledge.py:27 knowledge/serializers/knowledge.py:34 #: tools/serializers/tool.py:339 #| msgid "user id" msgid "folder id" msgstr "文件夹 ID" #: folders/serializers/folder.py:87 #| msgid "module user id" msgid "folder user id" msgstr "文件夹用户 ID" #: folders/serializers/folder.py:88 folders/serializers/folder.py:122 #: folders/serializers/folder.py:166 knowledge/serializers/knowledge.py:44 #: models_provider/api/model.py:40 models_provider/api/model.py:53 #: models_provider/serializers/model_serializer.py:262 #: models_provider/serializers/model_serializer.py:326 #: tools/serializers/tool.py:169 tools/serializers/tool.py:190 #: tools/serializers/tool.py:248 tools/serializers/tool.py:292 #: tools/serializers/tool.py:322 tools/serializers/tool.py:338 msgid "workspace id" msgstr "工作空间ID" #: folders/serializers/folder.py:92 knowledge/serializers/knowledge.py:43 #: models_provider/serializers/model_serializer.py:108 #: models_provider/serializers/model_serializer.py:215 #: models_provider/serializers/model_serializer.py:255 #: tools/serializers/tool.py:168 tools/serializers/tool.py:189 msgid "user id" msgstr "用户ID" #: folders/serializers/folder.py:93 folders/serializers/folder.py:123 #: folders/serializers/folder.py:167 tools/serializers/tool.py:97 msgid "source" msgstr "来源" #: folders/serializers/folder.py:106 #| msgid "Module name already exists" msgid "Folder name already exists" msgstr "文件夹名称已存在" #: folders/serializers/folder.py:132 #| msgid "Model does not exist" msgid "Folder does not exist" msgstr "文件夹不存在" #: folders/serializers/folder.py:160 #| msgid "Cannot delete root module" msgid "Cannot delete root folder" msgstr "无法删除根文件夹" #: folders/views/folder.py:19 folders/views/folder.py:20 #| msgid "Create model" msgid "Create folder" msgstr "创建文件夹" #: folders/views/folder.py:24 folders/views/folder.py:41 #: folders/views/folder.py:60 folders/views/folder.py:75 #: folders/views/folder.py:90 msgid "Folder" msgstr "文件夹" #: folders/views/folder.py:37 folders/views/folder.py:38 #| msgid "Get module tree" msgid "Get folder tree" msgstr "获取文件夹树" #: folders/views/folder.py:55 folders/views/folder.py:56 #| msgid "Update model" msgid "Update folder" msgstr "更新文件夹" #: folders/views/folder.py:71 folders/views/folder.py:72 #| msgid "Get module" msgid "Get folder" msgstr "获取文件夹" #: folders/views/folder.py:86 folders/views/folder.py:87 #| msgid "Delete model" msgid "Delete folder" msgstr "删除文件夹" #: knowledge/serializers/common.py:98 knowledge/serializers/knowledge.py:37 #| msgid "source" msgid "source url" msgstr "来源" #: knowledge/serializers/common.py:99 msgid "selector" msgstr "选择器" #: knowledge/serializers/common.py:106 #, python-brace-format msgid "URL error, cannot parse [{source_url}]" msgstr "URL 错误，无法解析 [{source_url}]" #: knowledge/serializers/common.py:114 #| msgid "init field list" msgid "id list" msgstr "ID 列表" #: knowledge/serializers/common.py:124 #| msgid "The following fields are required: {keys}" msgid "The following id does not exist: {error_id_list}" msgstr "以下ID不存在: {error_id_list}" #: knowledge/serializers/common.py:181 knowledge/serializers/common.py:205 msgid "The knowledge base is inconsistent with the vector model" msgstr "知识库与向量模型不一致" #: knowledge/serializers/common.py:183 knowledge/serializers/common.py:207 msgid "Knowledge base setting error, please reset the knowledge base" msgstr "知识库设置错误，请重置知识库" #: knowledge/serializers/common.py:212 #| msgid "model id" msgid "Model id" msgstr "模型ID" #: knowledge/serializers/common.py:213 msgid "Prompt word" msgstr "提示词" #: knowledge/serializers/common.py:215 msgid "state list" msgstr "状态列表" #: knowledge/serializers/document.py:26 #| msgid "module name" msgid "document name" msgstr "文档名称" #: knowledge/serializers/document.py:31 knowledge/serializers/knowledge.py:26 #: knowledge/serializers/knowledge.py:33 #| msgid "model name" msgid "knowledge name" msgstr "知识库名称" #: knowledge/serializers/document.py:32 knowledge/serializers/knowledge.py:28 #: knowledge/serializers/knowledge.py:35 #| msgid "tool description" msgid "knowledge description" msgstr "知识库描述" #: knowledge/serializers/document.py:33 #| msgid "Embedding Model" msgid "embedding model" msgstr "向量模型" #: knowledge/serializers/document.py:39 knowledge/serializers/document.py:90 #: knowledge/serializers/paragraph.py:58 knowledge/serializers/paragraph.py:150 #| msgid "parent id" msgid "document id" msgstr "文档 ID" #: knowledge/serializers/document.py:40 knowledge/serializers/paragraph.py:149 #| msgid "model name" msgid "knowledge id" msgstr "知识库 ID" #: knowledge/serializers/document.py:46 #| msgid "Module does not exist" msgid "document id not exist" msgstr "文档 ID 不存在" #: knowledge/serializers/document.py:71 #: models_provider/serializers/model_serializer.py:116 #: models_provider/serializers/model_serializer.py:132 #: models_provider/serializers/model_serializer.py:151 #: models_provider/serializers/model_serializer.py:178 #: models_provider/serializers/model_serializer.py:373 #: models_provider/tools.py:111 msgid "Model does not exist" msgstr "模型不存在" #: knowledge/serializers/document.py:73 #| msgid "No permission to access" msgid "No permission to use this model" msgstr "无权限使用此模型" #: knowledge/serializers/document.py:87 #| msgid "The user has been disabled, please contact the administrator!" msgid "The task is being executed, please do not send it repeatedly." msgstr "任务正在执行，请勿重复发送。" #: knowledge/serializers/document.py:95 #| msgid "Model does not exist" msgid "knowledge id not exist" msgstr "知识库 ID 不存在" #: knowledge/serializers/knowledge.py:29 knowledge/serializers/knowledge.py:36 msgid "knowledge embedding" msgstr "知识库向量" #: knowledge/serializers/knowledge.py:38 msgid "knowledge selector" msgstr "知识库选择器" #: knowledge/serializers/knowledge.py:55 msgid "" "The community version supports up to 50 knowledge bases. If you need more " "knowledge bases, please contact us (https://fit2cloud.com/)." msgstr "社区版支持最多50个知识库，如需更多知识库，请联系我们 (https://fit2cloud.com/)." #: knowledge/serializers/knowledge.py:64 knowledge/serializers/knowledge.py:123 msgid "Knowledge base name duplicate!" msgstr "知识库名称重复！" #: knowledge/serializers/paragraph.py:31 knowledge/serializers/problem.py:15 #| msgid "tool content" msgid "content" msgstr "内容" #: knowledge/serializers/paragraph.py:33 knowledge/serializers/paragraph.py:40 #: knowledge/serializers/paragraph.py:43 knowledge/serializers/paragraph.py:48 #: knowledge/serializers/paragraph.py:50 #| msgid "science fiction style" msgid "section title" msgstr "章节标题" #: knowledge/serializers/paragraph.py:36 tools/serializers/tool.py:127 #: tools/serializers/tool.py:147 msgid "Is active" msgstr "是否启用" #: knowledge/serializers/paragraph.py:54 msgid "paragraph id" msgstr "段落 ID" #: knowledge/serializers/paragraph.py:56 #| msgid "parent id" msgid "dataset id" msgstr "知识库 ID" #: knowledge/serializers/paragraph.py:63 #| msgid "Model does not exist" msgid "Paragraph id does not exist" msgstr "段落 ID 不存在" #: knowledge/serializers/paragraph.py:99 #| msgid "Model does not exist" msgid "Problem id does not exist" msgstr "问题 ID 不存在" #: knowledge/serializers/paragraph.py:156 #| msgid "The username or password is incorrect" msgid "The document id is incorrect" msgstr "文档 ID 不正确" #: knowledge/serializers/problem.py:14 msgid "problem id" msgstr "问题 ID" #: knowledge/task/embedding.py:24 knowledge/task/embedding.py:74 #, python-brace-format msgid "Failed to obtain vector model: {error} {traceback}" msgstr "向量模型获取失败: {error} {traceback}" #: knowledge/task/embedding.py:103 #, python-brace-format msgid "Start--->Vectorized knowledge: {knowledge_id}" msgstr "开始--->向量知识库: {knowledge_id}" #: knowledge/task/embedding.py:107 #, python-brace-format msgid "Knowledge documentation: {document_names}" msgstr "知识库文档: {document_names}" #: knowledge/task/embedding.py:120 #, python-brace-format msgid "End--->Vectorized knowledge: {knowledge_id}" msgstr "结束--->向量知识库: {knowledge_id}" #: knowledge/task/handler.py:107 #, python-brace-format msgid "Association problem failed {error}" msgstr "关联问题失败 {error}" #: knowledge/task/sync.py:29 knowledge/task/sync.py:44 #, python-brace-format msgid "Start--->Start synchronization web knowledge base:{knowledge_id}" msgstr "开始--->开始同步 web 知识库:{knowledge_id}" #: knowledge/task/sync.py:34 knowledge/task/sync.py:48 #, python-brace-format msgid "End--->End synchronization web knowledge base:{knowledge_id}" msgstr "结束--->结束同步 web 知识库:{knowledge_id}" #: knowledge/task/sync.py:36 knowledge/task/sync.py:50 #, python-brace-format msgid "Synchronize web knowledge base:{knowledge_id} error{error}{traceback}" msgstr "同步 web 知识库:{knowledge_id} 错误{error}{traceback}" #: knowledge/views/knowledge.py:19 knowledge/views/knowledge.py:20 #| msgid "Get module" msgid "Get knowledge by folder" msgstr "根据文件夹获取知识库" #: knowledge/views/knowledge.py:23 knowledge/views/knowledge.py:42 #: knowledge/views/knowledge.py:61 msgid "Knowledge Base" msgstr "知识库" #: knowledge/views/knowledge.py:37 knowledge/views/knowledge.py:38 #| msgid "Create model" msgid "Create base knowledge" msgstr "创建知识库" #: knowledge/views/knowledge.py:56 knowledge/views/knowledge.py:57 #| msgid "Create model" msgid "Create web knowledge" msgstr "创建 web 知识库" #: maxkb/settings/base.py:85 msgid "Intelligent customer service platform" msgstr "智能客服平台" #: models_provider/api/model.py:59 #: models_provider/serializers/model_serializer.py:107 #: models_provider/serializers/model_serializer.py:367 msgid "model id" msgstr "模型ID" #: models_provider/api/provide.py:17 models_provider/api/provide.py:23 #: models_provider/api/provide.py:28 models_provider/api/provide.py:30 #: models_provider/api/provide.py:82 #: models_provider/serializers/model_serializer.py:40 #: models_provider/serializers/model_serializer.py:218 #: models_provider/serializers/model_serializer.py:256 #: models_provider/serializers/model_serializer.py:321 msgid "model name" msgstr "模型名称" #: models_provider/api/provide.py:18 models_provider/api/provide.py:38 #: models_provider/api/provide.py:76 models_provider/api/provide.py:104 #: models_provider/api/provide.py:126 #: models_provider/serializers/model_serializer.py:41 #: models_provider/serializers/model_serializer.py:257 #: models_provider/serializers/model_serializer.py:324 msgid "provider" msgstr "供应商" #: models_provider/api/provide.py:19 msgid "icon" msgstr "图标" #: models_provider/api/provide.py:24 msgid "value" msgstr "值" #: models_provider/api/provide.py:29 models_provider/api/provide.py:70 #: models_provider/api/provide.py:98 #: models_provider/serializers/model_serializer.py:42 #: models_provider/serializers/model_serializer.py:220 #: models_provider/serializers/model_serializer.py:258 #: models_provider/serializers/model_serializer.py:322 msgid "model type" msgstr "模型类型" #: models_provider/api/provide.py:34 tools/serializers/tool.py:107 msgid "input type" msgstr "输入类型" #: models_provider/api/provide.py:35 msgid "label" msgstr "标签" #: models_provider/api/provide.py:36 msgid "text field" msgstr "文本字段" #: models_provider/api/provide.py:37 msgid "value field" msgstr "值" #: models_provider/api/provide.py:39 msgid "method" msgstr "方法" #: models_provider/api/provide.py:40 tools/serializers/tool.py:92 #: tools/serializers/tool.py:106 msgid "required" msgstr "必填" #: models_provider/api/provide.py:41 msgid "default value" msgstr "默认值" #: models_provider/api/provide.py:42 msgid "relation show field dict" msgstr "关系显示字段" #: models_provider/api/provide.py:43 msgid "relation trigger field dict" msgstr "关系触发字段" #: models_provider/api/provide.py:44 msgid "trigger type" msgstr "触发类型" #: models_provider/api/provide.py:45 msgid "attrs" msgstr "属性" #: models_provider/api/provide.py:46 msgid "props info" msgstr "props 信息" #: models_provider/base_model_provider.py:60 msgid "Model type cannot be empty" msgstr "模型类型不能为空" #: models_provider/base_model_provider.py:85 msgid "The current platform does not support downloading models" msgstr "当前平台不支持下载模型" #: models_provider/base_model_provider.py:143 msgid "LLM" msgstr "大语言模型" #: models_provider/base_model_provider.py:144 msgid "Embedding Model" msgstr "向量模型" #: models_provider/base_model_provider.py:145 msgid "Speech2Text" msgstr "语音识别" #: models_provider/base_model_provider.py:146 msgid "TTS" msgstr "语音合成" #: models_provider/base_model_provider.py:147 msgid "Vision Model" msgstr "视觉模型" #: models_provider/base_model_provider.py:148 msgid "Image Generation" msgstr "图片生成" #: models_provider/base_model_provider.py:149 msgid "Rerank" msgstr "重排模型" #: models_provider/base_model_provider.py:223 msgid "The model does not support" msgstr "模型不支持" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:42 msgid "" "With the GTE-Rerank text sorting series model developed by Alibaba Tongyi " "Lab, developers can integrate high-quality text retrieval and sorting " "through the LlamaIndex framework." msgstr "" "阿里巴巴通义实验室开发的GTE-Rerank文本排序系列模型，开发者可以通过LlamaIndex" "框架进行集成高质量文本检索、排序。" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:45 msgid "" "Chinese (including various dialects such as Cantonese), English, Japanese, " "and Korean support free switching between multiple languages." msgstr "中文（含粤语等各种方言）、英文、日语、韩语支持多个语种自由切换" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:48 msgid "" "CosyVoice is based on a new generation of large generative speech models, " "which can predict emotions, intonation, rhythm, etc. based on context, and " "has better anthropomorphic effects." msgstr "" "CosyVoice基于新一代生成式语音大模型，能根据上下文预测情绪、语调、韵律等，具有" "更好的拟人效果" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:51 msgid "" "Universal text vector is Tongyi Lab's multi-language text unified vector " "model based on the LLM base. It provides high-level vector services for " "multiple mainstream languages around the world and helps developers quickly " "convert text data into high-quality vector data." msgstr "" "通用文本向量，是通义实验室基于LLM底座的多语言文本统一向量模型，面向全球多个主" "流语种，提供高水准的向量服务，帮助开发者将文本数据快速转换为高质量的向量数" "据。" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:69 msgid "" "Tongyi Wanxiang - a large image model for text generation, supports " "bilingual input in Chinese and English, and supports the input of reference " "pictures for reference content or reference style migration. Key styles " "include but are not limited to watercolor, oil painting, Chinese painting, " "sketch, flat illustration, two-dimensional, and 3D. Cartoon." msgstr "" "通义万相-文本生成图像大模型，支持中英文双语输入，支持输入参考图片进行参考内容" "或者参考风格迁移，重点风格包括但不限于水彩、油画、中国画、素描、扁平插画、二" "次元、3D卡通。" #: models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py:95 msgid "Alibaba Cloud Bailian" msgstr "阿里云百炼" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/embedding.py:53 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/image.py:50 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:74 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/reranker.py:61 #: models_provider/impl/aliyun_bai_lian_model_provider/model/tti.py:43 #: models_provider/impl/aliyun_bai_lian_model_provider/model/tts.py:37 #: models_provider/impl/anthropic_model_provider/credential/image.py:33 #: models_provider/impl/anthropic_model_provider/credential/llm.py:57 #: models_provider/impl/aws_bedrock_model_provider/credential/embedding.py:34 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:53 #: models_provider/impl/azure_model_provider/credential/embedding.py:37 #: models_provider/impl/azure_model_provider/credential/image.py:40 #: models_provider/impl/azure_model_provider/credential/llm.py:69 #: models_provider/impl/deepseek_model_provider/credential/llm.py:57 #: models_provider/impl/gemini_model_provider/credential/embedding.py:36 #: models_provider/impl/gemini_model_provider/credential/image.py:32 #: models_provider/impl/gemini_model_provider/credential/llm.py:57 #: models_provider/impl/gemini_model_provider/model/stt.py:43 #: models_provider/impl/kimi_model_provider/credential/llm.py:57 #: models_provider/impl/local_model_provider/credential/embedding.py:36 #: models_provider/impl/local_model_provider/credential/reranker.py:37 #: models_provider/impl/ollama_model_provider/credential/embedding.py:37 #: models_provider/impl/ollama_model_provider/credential/reranker.py:44 #: models_provider/impl/openai_model_provider/credential/embedding.py:36 #: models_provider/impl/openai_model_provider/credential/image.py:35 #: models_provider/impl/openai_model_provider/credential/llm.py:59 #: models_provider/impl/siliconCloud_model_provider/credential/embedding.py:36 #: models_provider/impl/siliconCloud_model_provider/credential/image.py:35 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:58 #: models_provider/impl/siliconCloud_model_provider/credential/reranker.py:37 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:58 #: models_provider/impl/tencent_model_provider/credential/embedding.py:23 #: models_provider/impl/tencent_model_provider/credential/image.py:37 #: models_provider/impl/tencent_model_provider/credential/llm.py:51 #: models_provider/impl/tencent_model_provider/model/tti.py:54 #: models_provider/impl/vllm_model_provider/credential/embedding.py:36 #: models_provider/impl/vllm_model_provider/credential/llm.py:50 #: models_provider/impl/volcanic_engine_model_provider/credential/embedding.py:36 #: models_provider/impl/volcanic_engine_model_provider/credential/image.py:32 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:57 #: models_provider/impl/volcanic_engine_model_provider/model/tts.py:77 #: models_provider/impl/wenxin_model_provider/credential/embedding.py:31 #: models_provider/impl/wenxin_model_provider/credential/llm.py:60 #: models_provider/impl/xf_model_provider/credential/embedding.py:31 #: models_provider/impl/xf_model_provider/credential/llm.py:76 #: models_provider/impl/xf_model_provider/model/tts.py:101 #: models_provider/impl/xinference_model_provider/credential/embedding.py:31 #: models_provider/impl/xinference_model_provider/credential/image.py:32 #: models_provider/impl/xinference_model_provider/credential/llm.py:50 #: models_provider/impl/xinference_model_provider/credential/reranker.py:34 #: models_provider/impl/xinference_model_provider/model/tts.py:44 #: models_provider/impl/zhipu_model_provider/credential/image.py:31 #: models_provider/impl/zhipu_model_provider/credential/llm.py:56 #: models_provider/impl/zhipu_model_provider/model/tti.py:49 msgid "Hello" msgstr "你好" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/image.py:36 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:60 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/reranker.py:46 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/stt.py:44 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:96 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:89 #: models_provider/impl/anthropic_model_provider/credential/image.py:23 #: models_provider/impl/anthropic_model_provider/credential/llm.py:47 #: models_provider/impl/aws_bedrock_model_provider/credential/embedding.py:21 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:40 #: models_provider/impl/azure_model_provider/credential/embedding.py:27 #: models_provider/impl/azure_model_provider/credential/image.py:30 #: models_provider/impl/azure_model_provider/credential/llm.py:59 #: models_provider/impl/azure_model_provider/credential/stt.py:23 #: models_provider/impl/azure_model_provider/credential/tti.py:58 #: models_provider/impl/azure_model_provider/credential/tts.py:41 #: models_provider/impl/deepseek_model_provider/credential/llm.py:47 #: models_provider/impl/gemini_model_provider/credential/embedding.py:26 #: models_provider/impl/gemini_model_provider/credential/image.py:22 #: models_provider/impl/gemini_model_provider/credential/llm.py:47 #: models_provider/impl/gemini_model_provider/credential/stt.py:21 #: models_provider/impl/kimi_model_provider/credential/llm.py:47 #: models_provider/impl/local_model_provider/credential/embedding.py:27 #: models_provider/impl/local_model_provider/credential/reranker.py:28 #: models_provider/impl/ollama_model_provider/credential/embedding.py:26 #: models_provider/impl/ollama_model_provider/credential/image.py:19 #: models_provider/impl/ollama_model_provider/credential/llm.py:44 #: models_provider/impl/ollama_model_provider/credential/reranker.py:27 #: models_provider/impl/ollama_model_provider/credential/reranker.py:31 #: models_provider/impl/openai_model_provider/credential/embedding.py:26 #: models_provider/impl/openai_model_provider/credential/image.py:25 #: models_provider/impl/openai_model_provider/credential/llm.py:48 #: models_provider/impl/openai_model_provider/credential/stt.py:22 #: models_provider/impl/openai_model_provider/credential/tti.py:61 #: models_provider/impl/openai_model_provider/credential/tts.py:40 #: models_provider/impl/siliconCloud_model_provider/credential/embedding.py:26 #: models_provider/impl/siliconCloud_model_provider/credential/image.py:25 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:47 #: models_provider/impl/siliconCloud_model_provider/credential/reranker.py:28 #: models_provider/impl/siliconCloud_model_provider/credential/stt.py:22 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:61 #: models_provider/impl/siliconCloud_model_provider/credential/tts.py:22 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:47 #: models_provider/impl/tencent_model_provider/credential/embedding.py:19 #: models_provider/impl/tencent_model_provider/credential/image.py:28 #: models_provider/impl/tencent_model_provider/credential/llm.py:31 #: models_provider/impl/tencent_model_provider/credential/tti.py:78 #: models_provider/impl/vllm_model_provider/credential/embedding.py:26 #: models_provider/impl/vllm_model_provider/credential/image.py:22 #: models_provider/impl/vllm_model_provider/credential/llm.py:39 #: models_provider/impl/volcanic_engine_model_provider/credential/embedding.py:26 #: models_provider/impl/volcanic_engine_model_provider/credential/image.py:22 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:47 #: models_provider/impl/volcanic_engine_model_provider/credential/stt.py:25 #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:41 #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:51 #: models_provider/impl/wenxin_model_provider/credential/embedding.py:27 #: models_provider/impl/wenxin_model_provider/credential/llm.py:46 #: models_provider/impl/xf_model_provider/credential/embedding.py:27 #: models_provider/impl/xf_model_provider/credential/image.py:29 #: models_provider/impl/xf_model_provider/credential/llm.py:66 #: models_provider/impl/xf_model_provider/credential/stt.py:24 #: models_provider/impl/xf_model_provider/credential/tts.py:47 #: models_provider/impl/xinference_model_provider/credential/embedding.py:19 #: models_provider/impl/xinference_model_provider/credential/image.py:22 #: models_provider/impl/xinference_model_provider/credential/llm.py:39 #: models_provider/impl/xinference_model_provider/credential/reranker.py:25 #: models_provider/impl/xinference_model_provider/credential/stt.py:21 #: models_provider/impl/xinference_model_provider/credential/tti.py:59 #: models_provider/impl/xinference_model_provider/credential/tts.py:39 #: models_provider/impl/zhipu_model_provider/credential/image.py:21 #: models_provider/impl/zhipu_model_provider/credential/llm.py:47 #: models_provider/impl/zhipu_model_provider/credential/tti.py:40 #, python-brace-format msgid "{model_type} Model type is not supported" msgstr "{model_type} 模型类型不支持" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/image.py:44 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:68 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/reranker.py:55 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/stt.py:53 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:105 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:98 #, python-brace-format msgid "{key} is required" msgstr "{key} 是必填项" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/image.py:60 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:82 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/reranker.py:69 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/stt.py:67 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:121 #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:113 #: models_provider/impl/anthropic_model_provider/credential/image.py:43 #: models_provider/impl/anthropic_model_provider/credential/llm.py:65 #: models_provider/impl/aws_bedrock_model_provider/credential/embedding.py:42 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:61 #: models_provider/impl/azure_model_provider/credential/image.py:50 #: models_provider/impl/azure_model_provider/credential/stt.py:40 #: models_provider/impl/azure_model_provider/credential/tti.py:77 #: models_provider/impl/azure_model_provider/credential/tts.py:58 #: models_provider/impl/deepseek_model_provider/credential/llm.py:65 #: models_provider/impl/gemini_model_provider/credential/embedding.py:43 #: models_provider/impl/gemini_model_provider/credential/image.py:42 #: models_provider/impl/gemini_model_provider/credential/llm.py:66 #: models_provider/impl/gemini_model_provider/credential/stt.py:38 #: models_provider/impl/kimi_model_provider/credential/llm.py:64 #: models_provider/impl/local_model_provider/credential/embedding.py:44 #: models_provider/impl/local_model_provider/credential/reranker.py:45 #: models_provider/impl/ollama_model_provider/credential/reranker.py:51 #: models_provider/impl/openai_model_provider/credential/embedding.py:43 #: models_provider/impl/openai_model_provider/credential/image.py:45 #: models_provider/impl/openai_model_provider/credential/llm.py:67 #: models_provider/impl/openai_model_provider/credential/stt.py:39 #: models_provider/impl/openai_model_provider/credential/tti.py:80 #: models_provider/impl/openai_model_provider/credential/tts.py:58 #: models_provider/impl/siliconCloud_model_provider/credential/embedding.py:43 #: models_provider/impl/siliconCloud_model_provider/credential/image.py:45 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:66 #: models_provider/impl/siliconCloud_model_provider/credential/reranker.py:44 #: models_provider/impl/siliconCloud_model_provider/credential/stt.py:39 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:80 #: models_provider/impl/siliconCloud_model_provider/credential/tts.py:40 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:66 #: models_provider/impl/tencent_model_provider/credential/embedding.py:30 #: models_provider/impl/tencent_model_provider/credential/image.py:47 #: models_provider/impl/tencent_model_provider/credential/llm.py:57 #: models_provider/impl/tencent_model_provider/credential/tti.py:104 #: models_provider/impl/vllm_model_provider/credential/embedding.py:43 #: models_provider/impl/vllm_model_provider/credential/image.py:42 #: models_provider/impl/vllm_model_provider/credential/llm.py:55 #: models_provider/impl/volcanic_engine_model_provider/credential/embedding.py:43 #: models_provider/impl/volcanic_engine_model_provider/credential/image.py:42 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:66 #: models_provider/impl/volcanic_engine_model_provider/credential/stt.py:42 #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:58 #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:68 #: models_provider/impl/wenxin_model_provider/credential/embedding.py:38 #: models_provider/impl/xf_model_provider/credential/embedding.py:38 #: models_provider/impl/xf_model_provider/credential/image.py:50 #: models_provider/impl/xf_model_provider/credential/llm.py:84 #: models_provider/impl/xf_model_provider/credential/stt.py:41 #: models_provider/impl/xf_model_provider/credential/tts.py:65 #: models_provider/impl/xinference_model_provider/credential/image.py:41 #: models_provider/impl/xinference_model_provider/credential/reranker.py:40 #: models_provider/impl/xinference_model_provider/credential/stt.py:37 #: models_provider/impl/xinference_model_provider/credential/tti.py:77 #: models_provider/impl/xinference_model_provider/credential/tts.py:56 #: models_provider/impl/zhipu_model_provider/credential/image.py:41 #: models_provider/impl/zhipu_model_provider/credential/llm.py:64 #: models_provider/impl/zhipu_model_provider/credential/tti.py:59 #, python-brace-format msgid "" "Verification failed, please check whether the parameters are correct: {error}" msgstr "认证失败，请检查参数是否正确：{error}" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:17 #: models_provider/impl/anthropic_model_provider/credential/llm.py:22 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:14 #: models_provider/impl/azure_model_provider/credential/llm.py:23 #: models_provider/impl/deepseek_model_provider/credential/llm.py:22 #: models_provider/impl/gemini_model_provider/credential/llm.py:22 #: models_provider/impl/kimi_model_provider/credential/llm.py:22 #: models_provider/impl/ollama_model_provider/credential/llm.py:20 #: models_provider/impl/openai_model_provider/credential/llm.py:23 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:22 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:22 #: models_provider/impl/tencent_model_provider/credential/llm.py:14 #: models_provider/impl/vllm_model_provider/credential/llm.py:15 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:22 #: models_provider/impl/wenxin_model_provider/credential/llm.py:22 #: models_provider/impl/xf_model_provider/credential/llm.py:22 #: models_provider/impl/xf_model_provider/credential/llm.py:41 #: models_provider/impl/xinference_model_provider/credential/llm.py:15 #: models_provider/impl/zhipu_model_provider/credential/llm.py:22 msgid "Temperature" msgstr "温度" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:18 #: models_provider/impl/anthropic_model_provider/credential/llm.py:23 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:15 #: models_provider/impl/azure_model_provider/credential/llm.py:24 #: models_provider/impl/deepseek_model_provider/credential/llm.py:23 #: models_provider/impl/gemini_model_provider/credential/llm.py:23 #: models_provider/impl/kimi_model_provider/credential/llm.py:23 #: models_provider/impl/ollama_model_provider/credential/llm.py:21 #: models_provider/impl/openai_model_provider/credential/llm.py:24 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:23 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:23 #: models_provider/impl/tencent_model_provider/credential/llm.py:15 #: models_provider/impl/vllm_model_provider/credential/llm.py:16 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:23 #: models_provider/impl/wenxin_model_provider/credential/llm.py:23 #: models_provider/impl/xf_model_provider/credential/llm.py:23 #: models_provider/impl/xf_model_provider/credential/llm.py:42 #: models_provider/impl/xinference_model_provider/credential/llm.py:16 #: models_provider/impl/zhipu_model_provider/credential/llm.py:23 msgid "" "Higher values make the output more random, while lower values make it more " "focused and deterministic" msgstr "较高的数值会使输出更加随机，而较低的数值会使其更加集中和确定" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:30 #: models_provider/impl/anthropic_model_provider/credential/llm.py:31 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:23 #: models_provider/impl/azure_model_provider/credential/llm.py:32 #: models_provider/impl/azure_model_provider/credential/llm.py:43 #: models_provider/impl/deepseek_model_provider/credential/llm.py:31 #: models_provider/impl/gemini_model_provider/credential/llm.py:31 #: models_provider/impl/kimi_model_provider/credential/llm.py:31 #: models_provider/impl/ollama_model_provider/credential/llm.py:29 #: models_provider/impl/openai_model_provider/credential/llm.py:32 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:31 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:31 #: models_provider/impl/vllm_model_provider/credential/llm.py:24 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:31 #: models_provider/impl/wenxin_model_provider/credential/llm.py:31 #: models_provider/impl/xf_model_provider/credential/llm.py:31 #: models_provider/impl/xf_model_provider/credential/llm.py:50 #: models_provider/impl/xinference_model_provider/credential/llm.py:24 #: models_provider/impl/zhipu_model_provider/credential/llm.py:31 msgid "Output the maximum Tokens" msgstr "输出最大Token数" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:31 msgid "Specify the maximum number of tokens that the model can generate." msgstr "指定模型可以生成的最大 tokens 数" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:44 #: models_provider/impl/anthropic_model_provider/credential/image.py:15 #: models_provider/impl/anthropic_model_provider/credential/llm.py:74 msgid "API URL" msgstr "" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/llm.py:45 #: models_provider/impl/anthropic_model_provider/credential/image.py:16 #: models_provider/impl/anthropic_model_provider/credential/llm.py:75 msgid "API Key" msgstr "" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:20 #: models_provider/impl/azure_model_provider/credential/tti.py:15 #: models_provider/impl/openai_model_provider/credential/tti.py:15 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:15 #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:15 #: models_provider/impl/xinference_model_provider/credential/tti.py:14 #: models_provider/impl/zhipu_model_provider/credential/tti.py:15 #| msgid "page size" msgid "Image size" msgstr "每页大小" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:20 #: models_provider/impl/azure_model_provider/credential/tti.py:15 msgid "Specify the size of the generated image, such as: 1024x1024" msgstr "指定生成图片的尺寸, 如: 1024x1024" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:34 #: models_provider/impl/azure_model_provider/credential/tti.py:40 #: models_provider/impl/openai_model_provider/credential/tti.py:43 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:43 #: models_provider/impl/xinference_model_provider/credential/tti.py:41 msgid "Number of pictures" msgstr "图片数量" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:34 #: models_provider/impl/azure_model_provider/credential/tti.py:40 msgid "Specify the number of generated images" msgstr "指定生成图片的数量" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:44 msgid "Style" msgstr "风格" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:44 msgid "Specify the style of generated images" msgstr "指定生成图片的风格" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:48 msgid "Default value, the image style is randomly output by the model" msgstr "默认值，图片风格由模型随机输出" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:49 msgid "photography" msgstr "摄影" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:50 msgid "Portraits" msgstr "人像写真" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:51 msgid "3D cartoon" msgstr "3D卡通" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:52 msgid "animation" msgstr "动画" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:53 msgid "painting" msgstr "油画" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:54 msgid "watercolor" msgstr "水彩" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:55 msgid "sketch" msgstr "素描" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:56 msgid "Chinese painting" msgstr "中国画" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tti.py:57 msgid "flat illustration" msgstr "扁平插画" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:20 #| msgid "timbre" msgid "Timbre" msgstr "音色" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:20 #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:15 msgid "Chinese sounds can support mixed scenes of Chinese and English" msgstr "中文音色支持中英文混合场景" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:26 msgid "Long Xiaochun" msgstr "龙小淳" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:27 msgid "Long Xiaoxia" msgstr "龙小夏" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:28 msgid "Long Xiaochen" msgstr "龙小诚" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:29 msgid "Long Xiaobai" msgstr "龙小白" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:30 #| msgid "Long laotie" msgid "Long Laotie" msgstr "龙老铁" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:31 msgid "Long Shu" msgstr "龙书" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:32 msgid "Long Shuo" msgstr "龙硕" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:33 msgid "Long Jing" msgstr "龙婧" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:34 msgid "Long Miao" msgstr "龙妙" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:35 msgid "Long Yue" msgstr "龙悦" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:36 msgid "Long Yuan" msgstr "龙媛" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:37 msgid "Long Fei" msgstr "龙飞" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:38 msgid "Long Jielidou" msgstr "龙杰力豆" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:39 msgid "Long Tong" msgstr "龙彤" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:40 msgid "Long Xiang" msgstr "龙祥" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:47 msgid "Speaking speed" msgstr "语速" #: models_provider/impl/aliyun_bai_lian_model_provider/credential/tts.py:47 msgid "[0.5, 2], the default is 1, usually one decimal place is enough" msgstr "[0.5,2]，默认为1，通常一位小数就足够了" #: models_provider/impl/anthropic_model_provider/credential/image.py:28 #: models_provider/impl/anthropic_model_provider/credential/llm.py:52 #: models_provider/impl/azure_model_provider/credential/embedding.py:32 #: models_provider/impl/azure_model_provider/credential/image.py:35 #: models_provider/impl/azure_model_provider/credential/llm.py:64 #: models_provider/impl/azure_model_provider/credential/stt.py:28 #: models_provider/impl/azure_model_provider/credential/tti.py:63 #: models_provider/impl/azure_model_provider/credential/tts.py:46 #: models_provider/impl/deepseek_model_provider/credential/llm.py:52 #: models_provider/impl/gemini_model_provider/credential/embedding.py:31 #: models_provider/impl/gemini_model_provider/credential/image.py:27 #: models_provider/impl/gemini_model_provider/credential/llm.py:52 #: models_provider/impl/gemini_model_provider/credential/stt.py:26 #: models_provider/impl/kimi_model_provider/credential/llm.py:52 #: models_provider/impl/local_model_provider/credential/embedding.py:31 #: models_provider/impl/local_model_provider/credential/reranker.py:32 #: models_provider/impl/ollama_model_provider/credential/embedding.py:46 #: models_provider/impl/ollama_model_provider/credential/llm.py:62 #: models_provider/impl/ollama_model_provider/credential/reranker.py:63 #: models_provider/impl/openai_model_provider/credential/embedding.py:31 #: models_provider/impl/openai_model_provider/credential/image.py:30 #: models_provider/impl/openai_model_provider/credential/llm.py:53 #: models_provider/impl/openai_model_provider/credential/stt.py:27 #: models_provider/impl/openai_model_provider/credential/tti.py:66 #: models_provider/impl/openai_model_provider/credential/tts.py:45 #: models_provider/impl/siliconCloud_model_provider/credential/embedding.py:31 #: models_provider/impl/siliconCloud_model_provider/credential/image.py:30 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:52 #: models_provider/impl/siliconCloud_model_provider/credential/reranker.py:32 #: models_provider/impl/siliconCloud_model_provider/credential/stt.py:27 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:66 #: models_provider/impl/siliconCloud_model_provider/credential/tts.py:27 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:52 #: models_provider/impl/tencent_model_provider/credential/image.py:32 #: models_provider/impl/vllm_model_provider/credential/embedding.py:31 #: models_provider/impl/vllm_model_provider/credential/image.py:27 #: models_provider/impl/vllm_model_provider/credential/llm.py:65 #: models_provider/impl/volcanic_engine_model_provider/credential/embedding.py:31 #: models_provider/impl/volcanic_engine_model_provider/credential/image.py:27 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:52 #: models_provider/impl/volcanic_engine_model_provider/credential/stt.py:30 #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:46 #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:56 #: models_provider/impl/wenxin_model_provider/credential/llm.py:55 #: models_provider/impl/wenxin_model_provider/credential/llm.py:72 #: models_provider/impl/xf_model_provider/credential/image.py:34 #: models_provider/impl/xf_model_provider/credential/llm.py:71 #: models_provider/impl/xf_model_provider/credential/stt.py:29 #: models_provider/impl/xf_model_provider/credential/tts.py:52 #: models_provider/impl/xinference_model_provider/credential/embedding.py:40 #: models_provider/impl/xinference_model_provider/credential/image.py:27 #: models_provider/impl/xinference_model_provider/credential/llm.py:59 #: models_provider/impl/xinference_model_provider/credential/reranker.py:29 #: models_provider/impl/xinference_model_provider/credential/stt.py:26 #: models_provider/impl/xinference_model_provider/credential/tti.py:64 #: models_provider/impl/xinference_model_provider/credential/tts.py:44 #: models_provider/impl/zhipu_model_provider/credential/image.py:26 #: models_provider/impl/zhipu_model_provider/credential/llm.py:51 #: models_provider/impl/zhipu_model_provider/credential/tti.py:45 #, python-brace-format msgid "{key} is required" msgstr "{key} 是必填项" #: models_provider/impl/anthropic_model_provider/credential/llm.py:32 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:24 #: models_provider/impl/azure_model_provider/credential/llm.py:33 #: models_provider/impl/azure_model_provider/credential/llm.py:44 #: models_provider/impl/deepseek_model_provider/credential/llm.py:32 #: models_provider/impl/gemini_model_provider/credential/llm.py:32 #: models_provider/impl/kimi_model_provider/credential/llm.py:32 #: models_provider/impl/ollama_model_provider/credential/llm.py:30 #: models_provider/impl/openai_model_provider/credential/llm.py:33 #: models_provider/impl/siliconCloud_model_provider/credential/llm.py:32 #: models_provider/impl/tencent_cloud_model_provider/credential/llm.py:32 #: models_provider/impl/vllm_model_provider/credential/llm.py:25 #: models_provider/impl/volcanic_engine_model_provider/credential/llm.py:32 #: models_provider/impl/wenxin_model_provider/credential/llm.py:32 #: models_provider/impl/xf_model_provider/credential/llm.py:32 #: models_provider/impl/xf_model_provider/credential/llm.py:51 #: models_provider/impl/xinference_model_provider/credential/llm.py:25 #: models_provider/impl/zhipu_model_provider/credential/llm.py:32 msgid "Specify the maximum number of tokens that the model can generate" msgstr "指定模型可以生成的最大 tokens 数" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:36 msgid "" "An update to Claude 2 that doubles the context window and improves " "reliability, hallucination rates, and evidence-based accuracy in long " "documents and RAG contexts." msgstr "" "Claude 2 的更新，采用双倍的上下文窗口，并在长文档和 RAG 上下文中提高可靠性、" "幻觉率和循证准确性。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:43 msgid "" "Anthropic is a powerful model that can handle a variety of tasks, from " "complex dialogue and creative content generation to detailed command " "obedience." msgstr "" "Anthropic 功能强大的模型，可处理各种任务，从复杂的对话和创意内容生成到详细的" "指令服从。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:50 msgid "" "The Claude 3 Haiku is Anthropic's fastest and most compact model, with near-" "instant responsiveness. The model can answer simple queries and requests " "quickly. Customers will be able to build seamless AI experiences that mimic " "human interactions. Claude 3 Haiku can process images and return text " "output, and provides 200K context windows." msgstr "" "Claude 3 Haiku 是 Anthropic 最快速、最紧凑的模型，具有近乎即时的响应能力。该" "模型可以快速回答简单的查询和请求。客户将能够构建模仿人类交互的无缝人工智能体" "验。 Claude 3 Haiku 可以处理图像和返回文本输出，并且提供 200K 上下文窗口。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:57 msgid "" "The Claude 3 Sonnet model from Anthropic strikes the ideal balance between " "intelligence and speed, especially when it comes to handling enterprise " "workloads. This model offers maximum utility while being priced lower than " "competing products, and it's been engineered to be a solid choice for " "deploying AI at scale." msgstr "" "Anthropic 推出的 Claude 3 Sonnet 模型在智能和速度之间取得理想的平衡，尤其是在" "处理企业工作负载方面。该模型提供最大的效用，同时价格低于竞争产品，并且其经过" "精心设计，是大规模部署人工智能的可靠选择。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:64 msgid "" "The Claude 3.5 Sonnet raises the industry standard for intelligence, " "outperforming competing models and the Claude 3 Opus in extensive " "evaluations, with the speed and cost-effectiveness of our mid-range models." msgstr "" "Claude 3.5 Sonnet提高了智能的行业标准，在广泛的评估中超越了竞争对手的型号和" "Claude 3 Opus，具有我们中端型号的速度和成本效益。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:71 msgid "" "A faster, more affordable but still very powerful model that can handle a " "range of tasks including casual conversation, text analysis, summarization " "and document question answering." msgstr "" "一种更快速、更实惠但仍然非常强大的模型，它可以处理一系列任务，包括随意对话、" "文本分析、摘要和文档问题回答。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:78 msgid "" "Titan Text Premier is the most powerful and advanced model in the Titan Text " "series, designed to deliver exceptional performance for a variety of " "enterprise applications. With its cutting-edge features, it delivers greater " "accuracy and outstanding results, making it an excellent choice for " "organizations looking for a top-notch text processing solution." msgstr "" "Titan Text Premier 是 Titan Text 系列中功能强大且先进的型号，旨在为各种企业应" "用程序提供卓越的性能。凭借其尖端功能，它提供了更高的准确性和出色的结果，使其" "成为寻求一流文本处理解决方案的组织的绝佳选择。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:85 msgid "" "Amazon Titan Text Lite is a lightweight, efficient model ideal for fine-" "tuning English-language tasks, including summarization and copywriting, " "where customers require smaller, more cost-effective, and highly " "customizable models." msgstr "" "Amazon Titan Text Lite 是一种轻量级的高效模型，非常适合英语任务的微调，包括摘" "要和文案写作等，在这种场景下，客户需要更小、更经济高效且高度可定制的模型" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:91 msgid "" "Amazon Titan Text Express has context lengths of up to 8,000 tokens, making " "it ideal for a variety of high-level general language tasks, such as open-" "ended text generation and conversational chat, as well as support in " "retrieval-augmented generation (RAG). At launch, the model is optimized for " "English, but other languages are supported." msgstr "" "Amazon Titan Text Express 的上下文长度长达 8000 个 tokens，因而非常适合各种高" "级常规语言任务，例如开放式文本生成和对话式聊天，以及检索增强生成（RAG）中的支" "持。在发布时，该模型针对英语进行了优化，但也支持其他语言。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:97 msgid "" "7B dense converter for rapid deployment and easy customization. Small in " "size yet powerful in a variety of use cases. Supports English and code, as " "well as 32k context windows." msgstr "" "7B 密集型转换器，可快速部署，易于定制。体积虽小，但功能强大，适用于各种用例。" "支持英语和代码，以及 32k 的上下文窗口。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:103 msgid "" "Advanced Mistral AI large-scale language model capable of handling any " "language task, including complex multilingual reasoning, text understanding, " "transformation, and code generation." msgstr "" "先进的 Mistral AI 大型语言模型，能够处理任何语言任务，包括复杂的多语言推理、" "文本理解、转换和代码生成。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:109 msgid "" "Ideal for content creation, conversational AI, language understanding, R&D, " "and enterprise applications" msgstr "非常适合内容创作、会话式人工智能、语言理解、研发和企业应用" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:115 msgid "" "Ideal for limited computing power and resources, edge devices, and faster " "training times." msgstr "非常适合有限的计算能力和资源、边缘设备和更快的训练时间。" #: models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py:123 msgid "" "Titan Embed Text is the largest embedding model in the Amazon Titan Embed " "series and can handle various text embedding tasks, such as text " "classification, text similarity calculation, etc." msgstr "" "Titan Embed Text 是 Amazon Titan Embed 系列中最大的嵌入模型，可以处理各种文本" "嵌入任务，如文本分类、文本相似度计算等。" #: models_provider/impl/aws_bedrock_model_provider/credential/embedding.py:28 #: models_provider/impl/aws_bedrock_model_provider/credential/llm.py:47 #, python-brace-format msgid "The following fields are required: {keys}" msgstr "以下字段是必填项: {keys}" #: models_provider/impl/azure_model_provider/credential/embedding.py:44 #: models_provider/impl/azure_model_provider/credential/llm.py:76 msgid "Verification failed, please check whether the parameters are correct" msgstr "认证失败，请检查参数是否正确" #: models_provider/impl/azure_model_provider/credential/tti.py:28 #: models_provider/impl/openai_model_provider/credential/tti.py:29 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:29 #: models_provider/impl/xinference_model_provider/credential/tti.py:28 msgid "Picture quality" msgstr "图片质量" #: models_provider/impl/azure_model_provider/credential/tts.py:17 #: models_provider/impl/openai_model_provider/credential/tts.py:17 msgid "" "Try out the different sounds (Alloy, Echo, Fable, Onyx, Nova, and Sparkle) " "to find one that suits your desired tone and audience. The current voiceover " "is optimized for English." msgstr "" "尝试不同的声音（合金、回声、寓言、缟玛瑙、新星和闪光），找到一种适合您所需的" "音调和听众的声音。当前的语音针对英语进行了优化。" #: models_provider/impl/deepseek_model_provider/deepseek_model_provider.py:24 msgid "Good at common conversational tasks, supports 32K contexts" msgstr "擅长通用对话任务，支持 32K 上下文" #: models_provider/impl/deepseek_model_provider/deepseek_model_provider.py:29 msgid "Good at handling programming tasks, supports 16K contexts" msgstr "擅长处理编程任务，支持 16K 上下文" #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:32 msgid "Latest Gemini 1.0 Pro model, updated with Google update" msgstr "最新的 Gemini 1.0 Pro 模型，更新了 Google 更新" #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:36 msgid "Latest Gemini 1.0 Pro Vision model, updated with Google update" msgstr "最新的Gemini 1.0 Pro Vision模型，随Google更新而更新" #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:43 #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:47 #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:54 #: models_provider/impl/gemini_model_provider/gemini_model_provider.py:58 msgid "Latest Gemini 1.5 Flash model, updated with Google updates" msgstr "最新的Gemini 1.5 Flash模型，随Google更新而更新" #: models_provider/impl/gemini_model_provider/model/stt.py:53 msgid "convert audio to text" msgstr "将音频转换为文本" #: models_provider/impl/local_model_provider/credential/embedding.py:53 #: models_provider/impl/local_model_provider/credential/reranker.py:54 msgid "Model catalog" msgstr "模型目录" #: models_provider/impl/local_model_provider/local_model_provider.py:39 msgid "local model" msgstr "本地模型" #: models_provider/impl/ollama_model_provider/credential/embedding.py:30 #: models_provider/impl/ollama_model_provider/credential/image.py:23 #: models_provider/impl/ollama_model_provider/credential/llm.py:48 #: models_provider/impl/ollama_model_provider/credential/reranker.py:35 #: models_provider/impl/vllm_model_provider/credential/llm.py:43 #: models_provider/impl/xinference_model_provider/credential/embedding.py:24 #: models_provider/impl/xinference_model_provider/credential/llm.py:44 msgid "API domain name is invalid" msgstr "API 域名无效" #: models_provider/impl/ollama_model_provider/credential/embedding.py:35 #: models_provider/impl/ollama_model_provider/credential/image.py:28 #: models_provider/impl/ollama_model_provider/credential/llm.py:53 #: models_provider/impl/ollama_model_provider/credential/reranker.py:40 #: models_provider/impl/vllm_model_provider/credential/llm.py:47 #: models_provider/impl/xinference_model_provider/credential/embedding.py:30 #: models_provider/impl/xinference_model_provider/credential/llm.py:48 msgid "The model does not exist, please download the model first" msgstr "模型不存在，请先下载模型" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:56 msgid "" "Llama 2 is a set of pretrained and fine-tuned generative text models ranging " "in size from 7 billion to 70 billion. This is a repository of 7B pretrained " "models. Links to other models can be found in the index at the bottom." msgstr "" "Llama 2 是一组经过预训练和微调的生成文本模型，其规模从 70 亿到 700 亿个不等。" "这是 7B 预训练模型的存储库。其他模型的链接可以在底部的索引中找到。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:60 msgid "" "Llama 2 is a set of pretrained and fine-tuned generative text models ranging " "in size from 7 billion to 70 billion. This is a repository of 13B pretrained " "models. Links to other models can be found in the index at the bottom." msgstr "" "Llama 2 是一组经过预训练和微调的生成文本模型，其规模从 70 亿到 700 亿个不等。" "这是 13B 预训练模型的存储库。其他模型的链接可以在底部的索引中找到。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:64 msgid "" "Llama 2 is a set of pretrained and fine-tuned generative text models ranging " "in size from 7 billion to 70 billion. This is a repository of 70B pretrained " "models. Links to other models can be found in the index at the bottom." msgstr "" "Llama 2 是一组经过预训练和微调的生成文本模型，其规模从 70 亿到 700 亿个不等。" "这是 70B 预训练模型的存储库。其他模型的链接可以在底部的索引中找到。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:68 msgid "" "Since the Chinese alignment of Llama2 itself is weak, we use the Chinese " "instruction set to fine-tune meta-llama/Llama-2-13b-chat-hf with LoRA so " "that it has strong Chinese conversation capabilities." msgstr "" "由于Llama2本身的中文对齐较弱，我们采用中文指令集，对meta-llama/Llama-2-13b-" "chat-hf进行LoRA微调，使其具备较强的中文对话能力。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:72 msgid "" "Meta Llama 3: The most capable public product LLM to date. 8 billion " "parameters." msgstr "Meta Llama 3：迄今为止最有能力的公开产品LLM。80亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:76 msgid "" "Meta Llama 3: The most capable public product LLM to date. 70 billion " "parameters." msgstr "Meta Llama 3：迄今为止最有能力的公开产品LLM。700亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:80 msgid "" "Compared with previous versions, qwen 1.5 0.5b has significantly enhanced " "the model's alignment with human preferences and its multi-language " "processing capabilities. Models of all sizes support a context length of " "32768 tokens. 500 million parameters." msgstr "" "qwen 1.5 0.5b 相较于以往版本，模型与人类偏好的对齐程度以及多语言处理能力上有" "显著增强。所有规模的模型都支持32768个tokens的上下文长度。5亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:84 msgid "" "Compared with previous versions, qwen 1.5 1.8b has significantly enhanced " "the model's alignment with human preferences and its multi-language " "processing capabilities. Models of all sizes support a context length of " "32768 tokens. 1.8 billion parameters." msgstr "" "qwen 1.5 1.8b 相较于以往版本，模型与人类偏好的对齐程度以及多语言处理能力上有" "显著增强。所有规模的模型都支持32768个tokens的上下文长度。18亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:88 msgid "" "Compared with previous versions, qwen 1.5 4b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "4 billion parameters." msgstr "" "qwen 1.5 4b 相较于以往版本，模型与人类偏好的对齐程度以及多语言处理能力上有显" "著增强。所有规模的模型都支持32768个tokens的上下文长度。40亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:93 msgid "" "Compared with previous versions, qwen 1.5 7b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "7 billion parameters." msgstr "" "qwen 1.5 7b 相较于以往版本，模型与人类偏好的对齐程度以及多语言处理能力上有显" "著增强。所有规模的模型都支持32768个tokens的上下文长度。70亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:97 msgid "" "Compared with previous versions, qwen 1.5 14b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "14 billion parameters." msgstr "" "qwen 1.5 14b 相较于以往版本，模型与人类偏好的对齐程度以及多语言处理能力上有显" "著增强。所有规模的模型都支持32768个tokens的上下文长度。140亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:101 msgid "" "Compared with previous versions, qwen 1.5 32b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "32 billion parameters." msgstr "" "qwen 1.5 32b 相较于以往版本，模型与人类偏好的对齐程度以及多语言处理能力上有显" "著增强。所有规模的模型都支持32768个tokens的上下文长度。320亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:105 msgid "" "Compared with previous versions, qwen 1.5 72b has significantly enhanced the " "model's alignment with human preferences and its multi-language processing " "capabilities. Models of all sizes support a context length of 32768 tokens. " "72 billion parameters." msgstr "" "qwen 1.5 72b 相较于以往版本，模型与人类偏好的对齐程度以及多语言处理能力上有显" "著增强。所有规模的模型都支持32768个tokens的上下文长度。720亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:109 msgid "" "Compared with previous versions, qwen 1.5 110b has significantly enhanced " "the model's alignment with human preferences and its multi-language " "processing capabilities. Models of all sizes support a context length of " "32768 tokens. 110 billion parameters." msgstr "" "qwen 1.5 110b 相较于以往版本，模型与人类偏好的对齐程度以及多语言处理能力上有" "显著增强。所有规模的模型都支持32768个tokens的上下文长度。1100亿参数。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:153 #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:193 msgid "" "Phi-3 Mini is Microsoft's 3.8B parameter, lightweight, state-of-the-art open " "model." msgstr "Phi-3 Mini是Microsoft的3.8B参数，轻量级，最先进的开放模型。" #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:162 #: models_provider/impl/ollama_model_provider/ollama_model_provider.py:197 msgid "" "A high-performance open embedding model with a large token context window." msgstr "一个具有大 tokens上下文窗口的高性能开放嵌入模型。" #: models_provider/impl/openai_model_provider/credential/tti.py:16 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:16 msgid "" "The image generation endpoint allows you to create raw images based on text " "prompts. When using the DALL·E 3, the image size can be 1024x1024, 1024x1792 " "or 1792x1024 pixels." msgstr "" "图像生成端点允许您根据文本提示创建原始图像。使用 DALL·E 3 时，图像的尺寸可以" "为 1024x1024、1024x1792 或 1792x1024 像素。" #: models_provider/impl/openai_model_provider/credential/tti.py:29 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:29 msgid "" " \n" "By default, images are produced in standard quality, but with DALL·E 3 you " "can set quality: \"hd\" to enhance detail. Square, standard quality images " "are generated fastest.\n" " " msgstr "" "默认情况下，图像以标准质量生成，但使用 DALL·E 3 时，您可以设置质量：“hd”以增" "强细节。方形、标准质量的图像生成速度最快。" #: models_provider/impl/openai_model_provider/credential/tti.py:44 #: models_provider/impl/siliconCloud_model_provider/credential/tti.py:44 msgid "" "You can use DALL·E 3 to request 1 image at a time (requesting more images by " "issuing parallel requests), or use DALL·E 2 with the n parameter to request " "up to 10 images at a time." msgstr "" "您可以使用 DALL·E 3 一次请求 1 个图像（通过发出并行请求来请求更多图像），或者" "使用带有 n 参数的 DALL·E 2 一次最多请求 10 个图像。" #: models_provider/impl/openai_model_provider/openai_model_provider.py:35 #: models_provider/impl/openai_model_provider/openai_model_provider.py:119 #: models_provider/impl/siliconCloud_model_provider/siliconCloud_model_provider.py:118 msgid "The latest gpt-3.5-turbo, updated with OpenAI adjustments" msgstr "最新的gpt-3.5-turbo，随OpenAI调整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:38 msgid "Latest gpt-4, updated with OpenAI adjustments" msgstr "最新的gpt-4，随OpenAI调整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:40 #: models_provider/impl/openai_model_provider/openai_model_provider.py:99 msgid "" "The latest GPT-4o, cheaper and faster than gpt-4-turbo, updated with OpenAI " "adjustments" msgstr "最新的GPT-4o，比gpt-4-turbo更便宜、更快，随OpenAI调整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:43 #: models_provider/impl/openai_model_provider/openai_model_provider.py:102 msgid "" "The latest gpt-4o-mini, cheaper and faster than gpt-4o, updated with OpenAI " "adjustments" msgstr "最新的gpt-4o-mini，比gpt-4o更便宜、更快，随OpenAI调整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:46 msgid "The latest gpt-4-turbo, updated with OpenAI adjustments" msgstr "最新的gpt-4-turbo，随OpenAI调整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:49 msgid "The latest gpt-4-turbo-preview, updated with OpenAI adjustments" msgstr "最新的gpt-4-turbo-preview，随OpenAI调整而更新" #: models_provider/impl/openai_model_provider/openai_model_provider.py:53 msgid "" "gpt-3.5-turbo snapshot on January 25, 2024, supporting context length 16,385 " "tokens" msgstr "2024年1月25日的gpt-3.5-turbo快照，支持上下文长度16,385 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:57 msgid "" "gpt-3.5-turbo snapshot on November 6, 2023, supporting context length 16,385 " "tokens" msgstr "2023年11月6日的gpt-3.5-turbo快照，支持上下文长度16,385 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:61 msgid "" "[Legacy] gpt-3.5-turbo snapshot on June 13, 2023, will be deprecated on June " "13, 2024" msgstr "[Legacy] 2023年6月13日的gpt-3.5-turbo快照，将于2024年6月13日弃用" #: models_provider/impl/openai_model_provider/openai_model_provider.py:65 msgid "" "gpt-4o snapshot on May 13, 2024, supporting context length 128,000 tokens" msgstr "2024年5月13日的gpt-4o快照，支持上下文长度128,000 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:69 msgid "" "gpt-4-turbo snapshot on April 9, 2024, supporting context length 128,000 " "tokens" msgstr "2024年4月9日的gpt-4-turbo快照，支持上下文长度128,000 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:72 msgid "" "gpt-4-turbo snapshot on January 25, 2024, supporting context length 128,000 " "tokens" msgstr "2024年1月25日的gpt-4-turbo快照，支持上下文长度128,000 tokens" #: models_provider/impl/openai_model_provider/openai_model_provider.py:75 msgid "" "gpt-4-turbo snapshot on November 6, 2023, supporting context length 128,000 " "tokens" msgstr "2023年11月6日的gpt-4-turbo快照，支持上下文长度128,000 tokens" #: models_provider/impl/tencent_cloud_model_provider/tencent_cloud_model_provider.py:58 msgid "Tencent Cloud" msgstr "腾讯云" #: models_provider/impl/tencent_model_provider/credential/llm.py:41 #: models_provider/impl/tencent_model_provider/credential/tti.py:88 #, python-brace-format msgid "{keys} is required" msgstr "{keys} 是必填项" #: models_provider/impl/tencent_model_provider/credential/tti.py:14 msgid "painting style" msgstr "绘画风格" #: models_provider/impl/tencent_model_provider/credential/tti.py:14 msgid "If not passed, the default value is 201 (Japanese anime style)" msgstr "如果未传递，则默认值为201（日本动漫风格）" #: models_provider/impl/tencent_model_provider/credential/tti.py:18 msgid "Not limited to style" msgstr "不限于风格" #: models_provider/impl/tencent_model_provider/credential/tti.py:19 msgid "ink painting" msgstr "水墨画" #: models_provider/impl/tencent_model_provider/credential/tti.py:20 msgid "concept art" msgstr "概念艺术" #: models_provider/impl/tencent_model_provider/credential/tti.py:21 msgid "Oil painting 1" msgstr "油画1" #: models_provider/impl/tencent_model_provider/credential/tti.py:22 msgid "Oil Painting 2 (Van Gogh)" msgstr "油画2（梵高）" #: models_provider/impl/tencent_model_provider/credential/tti.py:23 msgid "watercolor painting" msgstr "水彩画" #: models_provider/impl/tencent_model_provider/credential/tti.py:24 msgid "pixel art" msgstr "像素画" #: models_provider/impl/tencent_model_provider/credential/tti.py:25 msgid "impasto style" msgstr "厚涂风格" #: models_provider/impl/tencent_model_provider/credential/tti.py:26 msgid "illustration" msgstr "插图" #: models_provider/impl/tencent_model_provider/credential/tti.py:27 msgid "paper cut style" msgstr "剪纸风格" #: models_provider/impl/tencent_model_provider/credential/tti.py:28 msgid "Impressionism 1 (Monet)" msgstr "印象派1（莫奈）" #: models_provider/impl/tencent_model_provider/credential/tti.py:29 msgid "Impressionism 2" msgstr "印象派2" #: models_provider/impl/tencent_model_provider/credential/tti.py:31 msgid "classical portraiture" msgstr "古典肖像画" #: models_provider/impl/tencent_model_provider/credential/tti.py:32 msgid "black and white sketch" msgstr "黑白素描画" #: models_provider/impl/tencent_model_provider/credential/tti.py:33 msgid "cyberpunk" msgstr "赛博朋克" #: models_provider/impl/tencent_model_provider/credential/tti.py:34 msgid "science fiction style" msgstr "科幻风格" #: models_provider/impl/tencent_model_provider/credential/tti.py:35 msgid "dark style" msgstr "暗黑风格" #: models_provider/impl/tencent_model_provider/credential/tti.py:37 msgid "vaporwave" msgstr "蒸汽波" #: models_provider/impl/tencent_model_provider/credential/tti.py:38 msgid "Japanese animation" msgstr "日系动漫" #: models_provider/impl/tencent_model_provider/credential/tti.py:39 msgid "monster style" msgstr "怪兽风格" #: models_provider/impl/tencent_model_provider/credential/tti.py:40 msgid "Beautiful ancient style" msgstr "唯美古风" #: models_provider/impl/tencent_model_provider/credential/tti.py:41 msgid "retro anime" msgstr "复古动漫" #: models_provider/impl/tencent_model_provider/credential/tti.py:42 msgid "Game cartoon hand drawing" msgstr "游戏卡通手绘" #: models_provider/impl/tencent_model_provider/credential/tti.py:43 msgid "Universal realistic style" msgstr "通用写实风格" #: models_provider/impl/tencent_model_provider/credential/tti.py:50 msgid "Generate image resolution" msgstr "生成图像分辨率" #: models_provider/impl/tencent_model_provider/credential/tti.py:50 msgid "If not transmitted, the default value is 768:768." msgstr "不传默认使用768:768。" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:38 msgid "" "The most effective version of the current hybrid model, the trillion-level " "parameter scale MOE-32K long article model. Reaching the absolute leading " "level on various benchmarks, with complex instructions and reasoning, " "complex mathematical capabilities, support for function call, and " "application focus optimization in fields such as multi-language translation, " "finance, law, and medical care" msgstr "" "当前混元模型中效果最优版本，万亿级参数规模 MOE-32K 长文模型。在各种 " "benchmark 上达到绝对领先的水平，复杂指令和推理，具备复杂数学能力，支持 " "functioncall，在多语言翻译、金融法律医疗等领域应用重点优化" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:45 msgid "" "A better routing strategy is adopted to simultaneously alleviate the " "problems of load balancing and expert convergence. For long articles, the " "needle-in-a-haystack index reaches 99.9%" msgstr "" "采用更优的路由策略，同时缓解了负载均衡和专家趋同的问题。长文方面，大海捞针指" "标达到99.9%" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:51 msgid "" "Upgraded to MOE structure, the context window is 256k, leading many open " "source models in multiple evaluation sets such as NLP, code, mathematics, " "industry, etc." msgstr "" "升级为 MOE 结构，上下文窗口为 256k ，在 NLP，代码，数学，行业等多项评测集上领" "先众多开源模型" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:57 msgid "" "Hunyuan's latest version of the role-playing model, a role-playing model " "launched by Hunyuan's official fine-tuning training, is based on the Hunyuan " "model combined with the role-playing scene data set for additional training, " "and has better basic effects in role-playing scenes." msgstr "" "混元最新版角色扮演模型，混元官方精调训练推出的角色扮演模型，基于混元模型结合" "角色扮演场景数据集进行增训，在角色扮演场景具有更好的基础效果" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:63 msgid "" "Hunyuan's latest MOE architecture FunctionCall model has been trained with " "high-quality FunctionCall data and has a context window of 32K, leading in " "multiple dimensions of evaluation indicators." msgstr "" "混元最新 MOE 架构 FunctionCall 模型，经过高质量的 FunctionCall 数据训练，上下" "文窗口达 32K，在多个维度的评测指标上处于领先。" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:69 msgid "" "Hunyuan's latest code generation model, after training the base model with " "200B high-quality code data, and iterating on high-quality SFT data for half " "a year, the context long window length has been increased to 8K, and it " "ranks among the top in the automatic evaluation indicators of code " "generation in the five major languages; the five major languages In the " "manual high-quality evaluation of 10 comprehensive code tasks that consider " "all aspects, the performance is in the first echelon." msgstr "" "混元最新代码生成模型，经过 200B 高质量代码数据增训基座模型，迭代半年高质量 " "SFT 数据训练，上下文长窗口长度增大到 8K，五大语言代码生成自动评测指标上位居前" "列；五大语言10项考量各方面综合代码任务人工高质量评测上，性能处于第一梯队" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:77 msgid "" "Tencent's Hunyuan Embedding interface can convert text into high-quality " "vector data. The vector dimension is 1024 dimensions." msgstr "" "腾讯混元 Embedding 接口，可以将文本转化为高质量的向量数据。向量维度为1024维。" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:87 msgid "Mixed element visual model" msgstr "混元视觉模型" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:94 msgid "Hunyuan graph model" msgstr "混元生图模型" #: models_provider/impl/tencent_model_provider/tencent_model_provider.py:125 msgid "Tencent Hunyuan" msgstr "腾讯混元" #: models_provider/impl/vllm_model_provider/vllm_model_provider.py:24 #: models_provider/impl/vllm_model_provider/vllm_model_provider.py:42 msgid "Facebook’s 125M parameter model" msgstr "Facebook的125M参数模型" #: models_provider/impl/vllm_model_provider/vllm_model_provider.py:25 msgid "BAAI’s 7B parameter model" msgstr "BAAI的7B参数模型" #: models_provider/impl/vllm_model_provider/vllm_model_provider.py:26 msgid "BAAI’s 13B parameter mode" msgstr "BAAI的13B参数模型" #: models_provider/impl/volcanic_engine_model_provider/credential/tti.py:16 msgid "" "If the gap between width, height and 512 is too large, the picture rendering " "effect will be poor and the probability of excessive delay will increase " "significantly. Recommended ratio and corresponding width and height before " "super score: width*height" msgstr "" "宽、高与512差距过大，则出图效果不佳、延迟过长概率显著增加。超分前建议比例及对" "应宽高：width*height" #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:15 #: models_provider/impl/xinference_model_provider/credential/tts.py:15 msgid "timbre" msgstr "音色" #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:31 #: models_provider/impl/xf_model_provider/credential/tts.py:28 #| msgid "Speaking speed" msgid "speaking speed" msgstr "语速" #: models_provider/impl/volcanic_engine_model_provider/credential/tts.py:31 msgid "[0.2,3], the default is 1, usually one decimal place is enough" msgstr "[0.2,3]，默认为1，通常保留一位小数即可" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:39 #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:44 #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:88 msgid "" "The user goes to the model inference page of Volcano Ark to create an " "inference access point. Here, you need to enter ep-xxxxxxxxxx-yyyy to call " "it." msgstr "" "用户前往火山方舟的模型推理页面创建推理接入点，这里需要输入ep-xxxxxxxxxx-yyyy" "进行调用" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:59 msgid "Universal 2.0-Vincent Diagram" msgstr "通用2.0-文生图" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:64 msgid "Universal 2.0Pro-Vincent Chart" msgstr "通用2.0Pro-文生图" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:69 msgid "Universal 1.4-Vincent Chart" msgstr "通用1.4-文生图" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:74 msgid "Animation 1.3.0-Vincent Picture" msgstr "动漫1.3.0-文生图" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:79 msgid "Animation 1.3.1-Vincent Picture" msgstr "动漫1.3.1-文生图" #: models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py:113 msgid "volcano engine" msgstr "火山引擎" #: models_provider/impl/wenxin_model_provider/credential/llm.py:51 #, python-brace-format msgid "{model_name} The model does not support" msgstr "{model_name} 模型不支持" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:24 #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:53 msgid "" "ERNIE-Bot-4 is a large language model independently developed by Baidu. It " "covers massive Chinese data and has stronger capabilities in dialogue Q&A, " "content creation and generation." msgstr "" "ERNIE-Bot-4是百度自行研发的大语言模型，覆盖海量中文数据，具有更强的对话问答、" "内容创作生成等能力。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:27 msgid "" "ERNIE-Bot is a large language model independently developed by Baidu. It " "covers massive Chinese data and has stronger capabilities in dialogue Q&A, " "content creation and generation." msgstr "" "ERNIE-Bot是百度自行研发的大语言模型，覆盖海量中文数据，具有更强的对话问答、内" "容创作生成等能力。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:30 msgid "" "ERNIE-Bot-turbo is a large language model independently developed by Baidu. " "It covers massive Chinese data, has stronger capabilities in dialogue Q&A, " "content creation and generation, and has a faster response speed." msgstr "" "ERNIE-Bot-turbo是百度自行研发的大语言模型，覆盖海量中文数据，具有更强的对话问" "答、内容创作生成等能力，响应速度更快。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:33 msgid "" "BLOOMZ-7B is a well-known large language model in the industry. It was " "developed and open sourced by BigScience and can output text in 46 languages " "and 13 programming languages." msgstr "" "BLOOMZ-7B是业内知名的大语言模型，由BigScience研发并开源，能够以46种语言和13种" "编程语言输出文本。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:39 msgid "" "Llama-2-13b-chat was developed by Meta AI and is open source. It performs " "well in scenarios such as coding, reasoning and knowledge application. " "Llama-2-13b-chat is a native open source version with balanced performance " "and effect, suitable for conversation scenarios." msgstr "" "Llama-2-13b-chat由Meta AI研发并开源，在编码、推理及知识应用等场景表现优秀，" "Llama-2-13b-chat是性能与效果均衡的原生开源版本，适用于对话场景。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:42 msgid "" "Llama-2-70b-chat was developed by Meta AI and is open source. It performs " "well in scenarios such as coding, reasoning, and knowledge application. " "Llama-2-70b-chat is a native open source version with high-precision effects." msgstr "" "Llama-2-70b-chat由Meta AI研发并开源，在编码、推理及知识应用等场景表现优秀，" "Llama-2-70b-chat是高精度效果的原生开源版本。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:45 msgid "" "The Chinese enhanced version developed by the Qianfan team based on " "Llama-2-7b has performed well on Chinese knowledge bases such as CMMLU and C-" "EVAL." msgstr "" "千帆团队在Llama-2-7b基础上的中文增强版本，在CMMLU、C-EVAL等中文知识库上表现优" "异。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:49 msgid "" "Embedding-V1 is a text representation model based on Baidu Wenxin large " "model technology. It can convert text into a vector form represented by " "numerical values and can be used in text retrieval, information " "recommendation, knowledge mining and other scenarios. Embedding-V1 provides " "the Embeddings interface, which can generate corresponding vector " "representations based on input content. You can call this interface to input " "text into the model and obtain the corresponding vector representation for " "subsequent text processing and analysis." msgstr "" "Embedding-V1是一个基于百度文心大模型技术的文本表示模型，可以将文本转化为用数" "值表示的向量形式，用于文本检索、信息推荐、知识挖掘等场景。 Embedding-V1提供了" "Embeddings接口，可以根据输入内容生成对应的向量表示。您可以通过调用该接口，将" "文本输入到模型中，获取到对应的向量表示，从而进行后续的文本处理和分析。" #: models_provider/impl/wenxin_model_provider/wenxin_model_provider.py:66 msgid "Thousand sails large model" msgstr "千帆大模型" #: models_provider/impl/xf_model_provider/credential/image.py:42 msgid "Please outline this picture" msgstr "请描述这张图片" #: models_provider/impl/xf_model_provider/credential/tts.py:15 msgid "Speaker" msgstr "发音人" #: models_provider/impl/xf_model_provider/credential/tts.py:16 msgid "" "Speaker, optional value: Please go to the console to add a trial or purchase " "speaker. After adding, the speaker parameter value will be displayed." msgstr "" "发音人，可选值：请到控制台添加试用或购买发音人，添加后即显示发音人参数值" #: models_provider/impl/xf_model_provider/credential/tts.py:21 msgid "iFlytek Xiaoyan" msgstr "讯飞小燕" #: models_provider/impl/xf_model_provider/credential/tts.py:22 msgid "iFlytek Xujiu" msgstr "讯飞许久" #: models_provider/impl/xf_model_provider/credential/tts.py:23 msgid "iFlytek Xiaoping" msgstr "讯飞小萍" #: models_provider/impl/xf_model_provider/credential/tts.py:24 msgid "iFlytek Xiaojing" msgstr "讯飞小婧" #: models_provider/impl/xf_model_provider/credential/tts.py:25 msgid "iFlytek Xuxiaobao" msgstr "讯飞许小宝" #: models_provider/impl/xf_model_provider/credential/tts.py:28 msgid "Speech speed, optional value: [0-100], default is 50" msgstr "语速，可选值：[0-100]，默认为50" #: models_provider/impl/xf_model_provider/xf_model_provider.py:39 #: models_provider/impl/xf_model_provider/xf_model_provider.py:50 msgid "Chinese and English recognition" msgstr "中英文识别" #: models_provider/impl/xf_model_provider/xf_model_provider.py:66 msgid "iFlytek Spark" msgstr "讯飞星火" #: models_provider/impl/xinference_model_provider/credential/tti.py:15 msgid "" "The image generation endpoint allows you to create raw images based on text " "prompts. The dimensions of the image can be 1024x1024, 1024x1792, or " "1792x1024 pixels." msgstr "" "图像生成端点允许您根据文本提示创建原始图像。图像的尺寸可以为 1024x1024、" "1024x1792 或 1792x1024 像素。" #: models_provider/impl/xinference_model_provider/credential/tti.py:29 msgid "" "By default, images are generated in standard quality, you can set quality: " "\"hd\" to enhance detail. Square, standard quality images are generated " "fastest." msgstr "" "默认情况下，图像以标准质量生成，您可以设置质量：“hd”以增强细节。方形、标准质" "量的图像生成速度最快。" #: models_provider/impl/xinference_model_provider/credential/tti.py:42 msgid "" "You can request 1 image at a time (requesting more images by making parallel " "requests), or up to 10 images at a time using the n parameter." msgstr "" "您可以一次请求 1 个图像（通过发出并行请求来请求更多图像），或者使用 n 参数一" "次最多请求 10 个图像。" #: models_provider/impl/xinference_model_provider/credential/tts.py:20 msgid "Chinese female" msgstr "中文女" #: models_provider/impl/xinference_model_provider/credential/tts.py:21 msgid "Chinese male" msgstr "中文男" #: models_provider/impl/xinference_model_provider/credential/tts.py:22 msgid "Japanese male" msgstr "日语男" #: models_provider/impl/xinference_model_provider/credential/tts.py:23 msgid "Cantonese female" msgstr "粤语女" #: models_provider/impl/xinference_model_provider/credential/tts.py:24 msgid "English female" msgstr "英文女" #: models_provider/impl/xinference_model_provider/credential/tts.py:25 msgid "English male" msgstr "英文男" #: models_provider/impl/xinference_model_provider/credential/tts.py:26 msgid "Korean female" msgstr "韩语女" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:37 msgid "" "Code Llama is a language model specifically designed for code generation." msgstr "Code Llama 是一个专门用于代码生成的语言模型。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:44 msgid "" " \n" "Code Llama Instruct is a fine-tuned version of Code Llama's instructions, " "designed to perform specific tasks.\n" " " msgstr "" "Code Llama Instruct 是 Code Llama 的指令微调版本，专为执行特定任务而设计。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:53 msgid "" "Code Llama Python is a language model specifically designed for Python code " "generation." msgstr "Code Llama Python 是一个专门用于 Python 代码生成的语言模型。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:60 msgid "" "CodeQwen 1.5 is a language model for code generation with high performance." msgstr "CodeQwen 1.5 是一个用于代码生成的语言模型，具有较高的性能。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:67 msgid "CodeQwen 1.5 Chat is a chat model version of CodeQwen 1.5." msgstr "CodeQwen 1.5 Chat 是一个聊天模型版本的 CodeQwen 1.5。" #: models_provider/impl/xinference_model_provider/xinference_model_provider.py:74 msgid "Deepseek is a large-scale language model with 13 billion parameters." msgstr "Deepseek Chat 是一个聊天模型版本的 Deepseek。" #: models_provider/impl/zhipu_model_provider/credential/tti.py:16 msgid "" "Image size, only cogview-3-plus supports this parameter. Optional range: " "[1024x1024,768x1344,864x1152,1344x768,1152x864,1440x720,720x1440], the " "default is 1024x1024." msgstr "" "图片尺寸，仅 cogview-3-plus 支持该参数。可选范围：" "[1024x1024,768x1344,864x1152,1344x768,1152x864,1440x720,720x1440]，默认是" "1024x1024。" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:34 msgid "" "Have strong multi-modal understanding capabilities. Able to understand up to " "five images simultaneously and supports video content understanding" msgstr "具有强大的多模态理解能力。能够同时理解多达五张图像，并支持视频内容理解" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:37 msgid "" "Focus on single picture understanding. Suitable for scenarios requiring " "efficient image analysis" msgstr "专注于单图理解。适用于需要高效图像解析的场景" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:40 msgid "" "Focus on single picture understanding. Suitable for scenarios requiring " "efficient image analysis (free)" msgstr "专注于单图理解。适用于需要高效图像解析的场景(免费)" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:46 msgid "" "Quickly and accurately generate images based on user text descriptions. " "Resolution supports 1024x1024" msgstr "根据用户文字描述快速、精准生成图像。分辨率支持1024x1024" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:49 msgid "" "Generate high-quality images based on user text descriptions, supporting " "multiple image sizes" msgstr "根据用户文字描述生成高质量图像，支持多图片尺寸" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:52 msgid "" "Generate high-quality images based on user text descriptions, supporting " "multiple image sizes (free)" msgstr "根据用户文字描述生成高质量图像，支持多图片尺寸(免费)" #: models_provider/impl/zhipu_model_provider/zhipu_model_provider.py:75 msgid "zhipu AI" msgstr "智谱 AI" #: models_provider/serializers/model_serializer.py:43 #: models_provider/serializers/model_serializer.py:222 #: models_provider/serializers/model_serializer.py:259 #: models_provider/serializers/model_serializer.py:323 msgid "base model" msgstr "基础模型" #: models_provider/serializers/model_serializer.py:44 #: models_provider/serializers/model_serializer.py:260 msgid "parameter configuration" msgstr "参数配置" #: models_provider/serializers/model_serializer.py:45 #: models_provider/serializers/model_serializer.py:225 #: models_provider/serializers/model_serializer.py:261 #| msgid "Get current user information" msgid "certification information" msgstr "认证信息" #: models_provider/serializers/model_serializer.py:233 #: models_provider/serializers/model_serializer.py:272 #, python-brace-format msgid "base model【{model_name}】already exists" msgstr "模型【{model_name}】已存在" #: models_provider/serializers/model_serializer.py:312 msgid "Model saving failed" msgstr "模型保存失败" #: models_provider/serializers/model_serializer.py:325 msgid "create user" msgstr "创建者" #: models_provider/views/model.py:28 models_provider/views/model.py:29 #: models_provider/views/model.py:30 msgid "Create model" msgstr "创建模型" #: models_provider/views/model.py:31 models_provider/views/model.py:59 #: models_provider/views/model.py:77 models_provider/views/model.py:90 #: models_provider/views/model.py:102 models_provider/views/model.py:117 #: models_provider/views/model.py:130 models_provider/views/model.py:148 #: models_provider/views/model.py:164 models_provider/views/provide.py:25 #: models_provider/views/provide.py:49 models_provider/views/provide.py:64 #: models_provider/views/provide.py:83 models_provider/views/provide.py:101 msgid "Model" msgstr "模型" #: models_provider/views/model.py:54 models_provider/views/model.py:55 #: models_provider/views/model.py:56 msgid "Query model list" msgstr "查询模型列表" #: models_provider/views/model.py:71 models_provider/views/model.py:72 #: models_provider/views/model.py:73 msgid "Update model" msgstr "更新模型" #: models_provider/views/model.py:85 models_provider/views/model.py:86 #: models_provider/views/model.py:87 msgid "Delete model" msgstr "删除模型" #: models_provider/views/model.py:97 models_provider/views/model.py:98 #: models_provider/views/model.py:99 msgid "Query model details" msgstr "查询模型详情" #: models_provider/views/model.py:112 models_provider/views/model.py:113 #: models_provider/views/model.py:114 msgid "Get model parameter form" msgstr "获取模型参数表单" #: models_provider/views/model.py:124 models_provider/views/model.py:125 #: models_provider/views/model.py:126 msgid "Save model parameter form" msgstr "保存模型参数表单" #: models_provider/views/model.py:141 models_provider/views/model.py:143 #: models_provider/views/model.py:145 msgid "" "Query model meta information, this interface does not carry authentication " "information" msgstr "查询模型元信息，该接口不携带认证信息" #: models_provider/views/model.py:158 models_provider/views/model.py:159 #: models_provider/views/model.py:160 msgid "Pause model download" msgstr "下载模型暂停" #: models_provider/views/provide.py:21 models_provider/views/provide.py:22 #: models_provider/views/provide.py:23 msgid "Get a list of model suppliers" msgstr "获取模型供应商列表" #: models_provider/views/provide.py:44 models_provider/views/provide.py:45 #: models_provider/views/provide.py:46 msgid "Get a list of model types" msgstr "获取模型类型列表" #: models_provider/views/provide.py:59 models_provider/views/provide.py:60 #: models_provider/views/provide.py:61 msgid "Example of obtaining model list" msgstr "获取模型列表示例" #: models_provider/views/provide.py:78 models_provider/views/provide.py:79 #: models_provider/views/provide.py:80 msgid "Get model default parameters" msgstr "获取模型默认参数" #: models_provider/views/provide.py:96 models_provider/views/provide.py:97 #: models_provider/views/provide.py:98 msgid "Get the model creation form" msgstr "获取模型创建表单" #: tools/serializers/tool.py:91 tools/serializers/tool.py:153 msgid "variable name" msgstr "变量名称" #: tools/serializers/tool.py:93 msgid "type" msgstr "类型" #: tools/serializers/tool.py:95 msgid "fields only support string|int|dict|array|float" msgstr "字段仅支持字符串|整数|字典|数组|浮点数" #: tools/serializers/tool.py:99 msgid "The field only supports custom|reference" msgstr "字段仅支持自定义|引用" #: tools/serializers/tool.py:104 #| msgid "model name" msgid "field name" msgstr "字段名称" #: tools/serializers/tool.py:105 #| msgid "label" msgid "field label" msgstr "标签" #: tools/serializers/tool.py:115 tools/serializers/tool.py:133 #: tools/serializers/tool.py:340 msgid "tool name" msgstr "工具名称" #: tools/serializers/tool.py:118 tools/serializers/tool.py:136 msgid "tool description" msgstr "工具描述" #: tools/serializers/tool.py:120 tools/serializers/tool.py:138 #: tools/serializers/tool.py:158 msgid "tool content" msgstr "工具内容" #: tools/serializers/tool.py:123 tools/serializers/tool.py:141 #: tools/serializers/tool.py:160 msgid "input field list" msgstr "输入字段列表" #: tools/serializers/tool.py:125 tools/serializers/tool.py:143 #: tools/serializers/tool.py:161 msgid "init field list" msgstr "内置字段列表" #: tools/serializers/tool.py:145 tools/serializers/tool.py:162 msgid "init params" msgstr "内置参数" #: tools/serializers/tool.py:154 #| msgid "variable name" msgid "variable value" msgstr "变量名称" #: tools/serializers/tool.py:218 msgid "field has no value set" msgstr "字段未设置值" #: tools/serializers/tool.py:234 tools/serializers/tool.py:239 msgid "type error" msgstr "类型错误" #: tools/serializers/tool.py:242 #, python-brace-format msgid "Field: {name} Type: {_type} Value: {value} Type conversion error" msgstr "字段：{name} 类型：{_type} 值：{value} 类型转换错误" #: tools/serializers/tool.py:247 #| msgid "model id" msgid "tool id" msgstr "工具 ID" #: tools/serializers/tool.py:255 msgid "Tool not found" msgstr "工具不存在" #: tools/serializers/tool.py:290 msgid "file" msgstr "文件" #: tools/serializers/tool.py:291 users/api/user.py:39 users/api/user.py:51 #: users/api/user.py:67 users/serializers/user.py:262 msgid "User ID" msgstr "用户 ID" #: tools/serializers/tool.py:304 msgid "Unsupported file format" msgstr "不支持的文件格式" #: tools/serializers/tool.py:330 tools/serializers/tool.py:349 #| msgid "Module not found" msgid "Folder not found" msgstr "文件夹不存在" #: tools/serializers/tool.py:341 #| msgid "model type" msgid "tool type" msgstr "工具类型" #: tools/views/tool.py:21 tools/views/tool.py:22 msgid "Create tool" msgstr "创建工具" #: tools/views/tool.py:26 tools/views/tool.py:40 tools/views/tool.py:57 #: tools/views/tool.py:75 tools/views/tool.py:89 tools/views/tool.py:103 #: tools/views/tool.py:120 tools/views/tool.py:144 tools/views/tool.py:161 msgid "Tool" msgstr "工具" #: tools/views/tool.py:36 tools/views/tool.py:37 #| msgid "Get module" msgid "Get tool by folder" msgstr "通过文件夹获取工具" #: tools/views/tool.py:53 tools/views/tool.py:54 msgid "Debug Tool" msgstr "调试工具" #: tools/views/tool.py:70 tools/views/tool.py:71 #| msgid "Update model" msgid "Update tool" msgstr "更新工具" #: tools/views/tool.py:85 tools/views/tool.py:86 #| msgid "Create tool" msgid "Get tool" msgstr "获取工具" #: tools/views/tool.py:99 tools/views/tool.py:100 #| msgid "Delete model" msgid "Delete tool" msgstr "删除工具" #: tools/views/tool.py:116 tools/views/tool.py:117 msgid "Get tool list by pagination" msgstr "获取工具列表" #: tools/views/tool.py:139 tools/views/tool.py:140 #| msgid "Create tool" msgid "Import tool" msgstr "导入工具" #: tools/views/tool.py:157 tools/views/tool.py:158 #| msgid "Create tool" msgid "Export tool" msgstr "导出工具" #: users/api/user.py:90 #| msgid "Username" msgid "Email or Username" msgstr "邮箱或用户名" #: users/api/user.py:106 #| msgid "workspace id" msgid "Workspace ID" msgstr "工作空间 ID" #: users/serializers/login.py:27 users/serializers/user.py:40 #: users/serializers/user.py:87 msgid "Username" msgstr "用户名" #: users/serializers/login.py:28 users/serializers/user.py:41 #: users/serializers/user.py:99 users/serializers/user.py:228 msgid "Password" msgstr "密码" #: users/serializers/login.py:29 users/serializers/login.py:69 msgid "captcha" msgstr "验证码" #: users/serializers/login.py:36 msgid "token" msgstr "令牌" #: users/serializers/login.py:50 msgid "Captcha code error or expiration" msgstr "验证码错误或过期" #: users/serializers/login.py:55 msgid "The user has been disabled, please contact the administrator!" msgstr "用户已被禁用，请联系管理员！" #: users/serializers/user.py:31 #| msgid "Password" msgid "Is Edit Password" msgstr "是否编辑密码" #: users/serializers/user.py:32 #| msgid "No permission to access" msgid "permissions" msgstr "无权限访问" #: users/serializers/user.py:42 users/serializers/user.py:79 #: users/serializers/user.py:191 msgid "Email" msgstr "邮箱" #: users/serializers/user.py:43 users/serializers/user.py:113 #| msgid "model name" msgid "Nick name" msgstr "昵称" #: users/serializers/user.py:44 users/serializers/user.py:120 #: users/serializers/user.py:206 msgid "Phone" msgstr "手机" #: users/serializers/user.py:93 msgid "Username must be 6-20 characters long" msgstr "用户名必须为6-20个字符" #: users/serializers/user.py:106 users/serializers/user.py:235 msgid "" "The password must be 6-20 characters long and must be a combination of " "letters, numbers, and special characters." msgstr "密码必须为6-20个字符，且必须包含字母、数字和特殊字符。" #: users/serializers/user.py:142 msgid "Email or username" msgstr "邮箱或用户名" #: users/serializers/user.py:168 msgid "" "The community version supports up to 2 users. If you need more users, please " "contact us (https://fit2cloud.com/)." msgstr "社区版支持最多2个用户，如需更多用户，请联系我们（https://fit2cloud.com/）。" #: users/serializers/user.py:199 msgid "Name" msgstr "用户名" #: users/serializers/user.py:213 #, fuzzy #| msgid "Is active" msgid "Is Active" msgstr "是否启用" #: users/serializers/user.py:223 #| msgid "Model saving failed" msgid "Email is already in use" msgstr "邮箱已被使用" #: users/serializers/user.py:242 #| msgid "Password" msgid "Re Password" msgstr "确认密码" #: users/serializers/user.py:247 msgid "" "The confirmation password must be 6-20 characters long and must be a " "combination of letters, numbers, and special characters." msgstr "确认密码必须为6-20个字符，且必须包含字母、数字和特殊字符。" #: users/serializers/user.py:270 #| msgid "Model does not exist" msgid "User does not exist" msgstr "用户不存在" #: users/serializers/user.py:285 #| msgid "Super administrator" msgid "Unable to delete administrator" msgstr "无法删除管理员" #: users/serializers/user.py:302 msgid "Cannot modify administrator status" msgstr "不能修改管理员状态" #: users/views/login.py:21 users/views/login.py:22 users/views/login.py:23 msgid "Log in" msgstr "登录" #: users/views/login.py:24 users/views/login.py:36 users/views/user.py:31 #: users/views/user.py:44 users/views/user.py:58 users/views/user.py:73 #: users/views/user.py:87 users/views/user.py:98 users/views/user.py:109 #: users/views/user.py:125 users/views/user.py:140 msgid "User management" msgstr "用户管理" #: users/views/login.py:33 users/views/login.py:34 users/views/login.py:35 msgid "Get captcha" msgstr "获取验证码" #: users/views/user.py:28 users/views/user.py:29 users/views/user.py:30 #: users/views/user.py:41 users/views/user.py:42 msgid "Get current user information" msgstr "获取当前用户信息" #: users/views/user.py:70 users/views/user.py:71 users/views/user.py:72 #| msgid "create user" msgid "Create user" msgstr "创建者" #: users/views/user.py:84 users/views/user.py:85 users/views/user.py:86 #| msgid "Delete model" msgid "Delete user" msgstr "删除用户" #: users/views/user.py:95 users/views/user.py:96 users/views/user.py:97 #| msgid "Get current user information" msgid "Get user information" msgstr "获取用户信息" #: users/views/user.py:106 users/views/user.py:107 users/views/user.py:108 #| msgid "Get current user information" msgid "Update user information" msgstr "更新当前用户信息" #: users/views/user.py:122 users/views/user.py:123 users/views/user.py:124 #| msgid "Password" msgid "Change password" msgstr "修改密码" #: users/views/user.py:137 users/views/user.py:138 users/views/user.py:139 msgid "Get user paginated list" msgstr "获取用户分页列表"