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[Docs] Edit HF Inference Endpoints documentation (#26275)

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Signed-off-by: Aritra Roy Gosthipaty <aritra.born2fly@gmail.com>
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docs/deployment/frameworks/hf_inference_endpoints.md
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@ -61,7 +61,7 @@ This is the easiest way to get started with vLLM on Hugging Face Inference Endpo
### Method 2: Guided Deployment (Transformers Models)
This method applies to models with the `transformers` library tag in their metadata. It allows you to deploy a model directly from the Hub UI without manual configuration.
This method applies to models with the [`transformers` library tag](https://huggingface.co/models?library=transformers) in their metadata. It allows you to deploy a model directly from the Hub UI without manual configuration.
1. Navigate to a model on [Hugging Face Hub](https://huggingface.co/models).
For this example we will use the [`ibm-granite/granite-docling-258M`](https://huggingface.co/ibm-granite/granite-docling-258M) model. You can verify that the model is compatible by checking the front matter in the [README](https://huggingface.co/ibm-granite/granite-docling-258M/blob/main/README.md), where the library is tagged as `library: transformers`.
@ -128,7 +128,7 @@ Some models require manual deployment because they:
These models cannot be deployed using the **Deploy** button on the model card.
In this guide, we demonstrate manual deployment using the [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) model, an OCR model integrated with vLLM (see vLLM [PR](https://github.com/vllm-project/vllm/pull/24645)).
In this guide, we demonstrate manual deployment using the [`rednote-hilab/dots.ocr`](https://huggingface.co/rednote-hilab/dots.ocr) model, an OCR model integrated with vLLM (see vLLM [PR](https://github.com/vllm-project/vllm/pull/24645)).
1. Start a new deployment. Go to [Inference Endpoints](https://endpoints.huggingface.co/) and click `New`.