> 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-hk/integrations/llms-and-databases/huggingface-integration.md).
# HuggingFace 整合
在 HuggingFace 上部署您的推理端點後，您應該會看到以下用戶界面：
在此頁面上，您需要以下資訊：
* 端點 URL
* 模型庫
* API 令牌。您可以通過在呼叫範例代碼區塊中勾選「添加 API 令牌」框來查看此項。
除了這些，您還需要模型的上下文窗口。這可以在模型的資訊頁面上找到。
收集完這些資訊後，將其格式化為 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
}
```
接下來，將此內容粘貼到您的整合的憑證欄位中。
一旦憑證成功驗證，您應該會在 GenStudio 的模型列表中看到您的 HuggingFace 模型：
### 將 HuggingFace 端點擴展至零
擴展至零是由推理端點提供的動態功能，旨在優化資源使用和成本。通過智能監控請求模式並在空閒時間將副本數減少至無，確保您僅在需要時使用資源。
然而，當流量恢復時，這確實會引入一個冷啟動期，也有一些需要注意的事項。請參閱 [HuggingFace 的 Autoscaling 指南](https://huggingface.co/docs/inference-endpoints/autoscaling)以深入了解此功能的運作方式、其優點以及可能的挑戰。
### 支援的模型
目前，我們僅支援具有 `text-generation` 標籤的模型的端點，這些模型以 `text-generation-inference` 容器的形式部署。我們正在努力擴展我們支援的模型列表。
LLaMA 2 是一個具有文本生成標籤的模型
確保在部署期間選擇文本生成推理作為容器類型
---
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