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132fd1b84d
maxkb/apps/locales/zh_CN/LC_MESSAGES/django.po
wxg0103 132fd1b84d refactor: i18n
2025-04-29 15:45:47 +08:00

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# 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 <EMAIL@ADDRESS>, 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 <EMAIL@ADDRESS>\n"
"Language-Team: LANGUAGE <LL@li.org>\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"
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