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# 功能特性 | ||
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智能应用的功能特性参见下表: | ||
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| 主要功能 | 细分项 | | ||
| ------- | ------ | | ||
| 对话 | 支持基于已发布的应用关联语料库,实现智能问答功能 | | ||
| | 对AI提供的回答,用户可以进行评价,复制,重新生成,删除对话或提交反馈,高度互动 | | ||
| 应用中心 | 支持创建RAG知识问答应用,提供应用的全生命周期管理,以及绑定或解绑工作空间,实现环境隔离 | | ||
| | 支持使用GLM、Llama等模型服务作为应用的模型服务,实现RAG | | ||
| | 提供多种配置选项,包括AI配置、关联语料库、检索策略、召回策略和提示词模板,以优化AI回答的效果 | | ||
| | 支持创建应用的密钥,用于OpenAPI对话,保证安全性 | | ||
| 语料库 | 支持使用北京智源人工智能研究院的bge-large-zh-v1.5、bge-large-en-v1.5对语料进行特征提取 | | ||
| | 支持多种导入方式,包括标准导入、格式化导入、手动导入、图文导入 | | ||
| | 支持多种分片方式,包括按照分割符、分片大小进行分片 | | ||
| | 支持使用插件对文件进行自定义分片 | | ||
| | 支持设置语料库的访问级别,保证数据隔离 | | ||
| | 支持CSV和Excel格式导出语料库分片,方便后续处理和分析 | | ||
| 数据分析 | 提供问答质量、问答次数、分片质量、分片命中率等数据分析 | | ||
| | 收集并处理用户反馈意见,以便改进服务 | | ||
| 我的反馈 | 保存用户提交过的反馈信息,记录用户的使用体验 | | ||
| 系统配置 | 提供配置聊天记忆轮数、提示词模板等系统配置选项 | |
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# 功能特性 | ||
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模型中心的功能特性参见下表: | ||
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| 主要功能 | 细分项 | | ||
| ------- | ------ | | ||
| 模型管理 | 提供图形化界面,能够直观地进行GLM系列模型的推理 | | ||
| | 提供图形化界面,能够直观地进行Llama系列模型的推理 | | ||
| | 提供图形化界面,能够直观地进行百川系列模型的推理 | | ||
| | 提供图形化界面,能够直观地进行文心一言系列模型的推理 | | ||
| | 支持查看模型微调出来的模型 | | ||
| | 可视化进行微调模型推理 | | ||
| | 提供图形化界面,能够直观地进行微调模型推理的对话内容对比 | | ||
| 模型服务 | 提供全面的本地模型服务管理,覆盖模型服务的整个生命周期 | | ||
| | 提供在线模型服务的API key管理,并实现智能负载均衡,提升使用效率 | |
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# 功能特性 | ||
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模型微调的功能特性参见下表: | ||
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| 主要功能 | 细分项 | | ||
| ------- | ------ | | ||
| 微调实验 | 提供直观的界面,可视化地进行Llama系列模型(7B/13B)的指令微调 | | ||
| | 支持跨GPU的模型分布式微调,提升训练效率 | | ||
| | 支持使用不同的参数组和数据集创建微调实验,进行交叉微调 | | ||
| | 支持查看微调过程中的学习率、训练损失和验证损失等关键数据,实时监控模型训练状态 | | ||
| | 支持对检查点(checkpoint)进行评估打分,保证模型质量 | | ||
| | 支持将模型存储到模型中心,提供可视化的模型列表和训练参数查看 | | ||
| 数据集 | 提供直观的界面,可视化地创建数据集,包括训练、验证和测试数据集 | | ||
| | 支持直接从S3存储中拉取文件,方便快捷 | | ||
| | 支持本地文件上传,提供多种数据接入方式 | | ||
| 参数组 | 提供直观的界面,可视化地创建超参数组,包括调度器(Scheduler)、优化器(Optimizer)、学习率(LearningRate)、训练周期(Epochs)和批次大小(BatchSize)等 | |
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