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# 内置模型 | ||
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内置模型是 d.run 按照规范适配验证后上线到模型中心的,您可以直接部署。 | ||
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如果想要部署某个模型, | ||
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1. 在模型卡片右下角,点击 **部署** 按钮。 | ||
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![点击部署按钮](../images/inner02.png) | ||
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2. 填写各项参数后点击 **确定** 。 | ||
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![填写参数](../images/inner03.png) | ||
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!!! info "支持国产 GPU" | ||
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其中算力类型支持 Nvidia GPU 和 Ascend 等国产 GPU。 | ||
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3. 屏幕提示创建成功,接下来可以通过部署的模型提供服务。 | ||
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下一步:[模型服务](../model-service/index.md) |
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# 模型仓库 | ||
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模型仓库中存放了已经适配好、经验证可以直接使用的大模型。 | ||
目前包括 2 种模型: | ||
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- [内置模型](./built-in.md) | ||
- [微调模型](./tuner.md) | ||
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## 切换集群 | ||
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在导航栏左上角,可以切换集群/算力中心。 | ||
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![切换集群](../images/inner01.png) |
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# 微调模型 | ||
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微调模型是指上线到模型仓库后,又通过 [DataTunerX](https://github.com/DataTunerX/datatunerx) 等工具的微调实验得出的模型。 | ||
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## 部署 | ||
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1. 点击某个模型右侧的 **...**,在弹出菜单中选择 **部署** 。 | ||
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![点击部署按钮](../images/tuner01.png) | ||
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2. 填写各项参数后点击 **确定** 。 | ||
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![填写参数](../images/inner03.png) | ||
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!!! info "支持国产 GPU" | ||
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其中算力类型支持 Nvidia GPU 和 Ascend 等国产 GPU。 | ||
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3. 屏幕提示创建成功,接下来可以通过部署的模型提供服务。 | ||
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下一步:[模型服务](../model-service/index.md) | ||
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## 删除 | ||
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1. 点击某个模型右侧的 **...**,在弹出菜单中选择 **删除** 。 | ||
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![点击删除按钮](../images/inner02.png) | ||
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2. 输入模型名称,确认无误后删除。 | ||
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!!! note | ||
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删除操作不可逆,请谨慎操作。 |
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# 模型服务 | ||
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模型服务从地理位置可分为: | ||
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- 本地模型 | ||
- 在线模型 | ||
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从类型上可划分为: | ||
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- 大语言模型:即 Large Language Model (LLM),这是基于大量数据进行预训练的超大型深度学习模型 | ||
- 向量化模型:这种模型将数据表示成计算机可识别的实数向量(vector),根据粒度大小不同可将数据特征表示分为字、词、句子或篇章几个层次。 | ||
数据向量化的方法主要分为离散表示和分布式表示。也就是说,数据向量化是将原始数据转换为数值向量的过程,以便计算机可以理解和处理数据。 |
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# 本地模型服务 |
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# 在线模型服务 |
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