> For the complete documentation index, see [llms.txt](https://docs.skydeck.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.skydeck.ai/zh-cn/integrations/llms-and-databases/huggingface-integration.md).
# HuggingFace 集成
在 HuggingFace 上部署您的推理端点后，您应该会看到以下用户界面：
在此页面上，您需要以下信息：
* 端点 URL
* 模型库
* API 令牌。您可以通过在调用示例代码块中勾选“添加 API 令牌”框来查看此信息。
除了这些，您还需要模型的上下文窗口。您可以在模型的信息页面找到此信息。
收集完这些信息后，将其格式化为 JSON，如下例所示：
```json
{
"api_key":"your_api_key",
"endpoint": "your_api_endpoint",
"model_name": "meta-llama/Llama-2-7b-chat-hf",
"context_window": 4096
}
```
接下来，将其粘贴到您的集成的凭据字段中。
一旦凭据成功验证，您应该会在 GenStudio 的模型列表中看到您的 HuggingFace 模型：
### 将HuggingFace端点缩减到零
缩减到0是Inference Endpoints提供的一种动态特性，旨在优化资源利用和成本。通过智能监控请求模式并在空闲时间将副本数量减少到零，确保您只在必要时使用资源。
然而，这确实引入了一个冷启动期，当流量恢复时，有几个需要注意的考虑因素。要深入了解这个特性的功能，其优点以及潜在挑战，请参考[HuggingFace的自动缩放指南](https://huggingface.co/docs/inference-endpoints/autoscaling)。
### 支持的模型
目前，我们仅支持带有 `text-generation` 标签并作为 `text-generation-inference` 容器部署的模型的端点。我们正在努力扩展我们支持的模型列表。
LLaMA 2 是一个带有文本生成标签的模型
确保在部署期间选择文本生成推理作为容器类型
---
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