Add pipeline tag and library name

This PR adds the `pipeline_tag` and `library_name` to the model card metadata for better discoverability and clarity. The `pipeline_tag` is set to `image-feature-extraction` reflecting the model's function, and `library_name` is set to `transformers` as the model is used via the Transformers library.

README.md

@@ -1,41 +1,42 @@
----
-license: mit
----
-
-
-<div align="center">
-  <img width="30%" src="figures/logo.png">
-</div>
-
-
-## Introduction
-
-**MoonViT** is a Native-resolution Vision Encoder, which is initialized from and continually pre-trained on **SigLIP-SO-400M**.
-To facilitate the standalone use of MoonViT, we have separated the implementation and weights of MoonViT from [moonshotai/Kimi-VL-A3B-Instruct](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct).
-
-If you are interested in the training process of MoonViT, you are welcome to read Paper [Kimi-VL Technical Report](https://huggingface.co/papers/2504.07491).
-
-## Example usage
-
-```python
-from PIL import Image
-from transformers import AutoModel, AutoImageProcessor
-
-model_path = "moonshotai/MoonViT-SO-400M"
-model = AutoModel.from_pretrained(
-    model_path,
-    torch_dtype="auto",
-    device_map="auto",
-    trust_remote_code=True,
-)
-processor = AutoImageProcessor.from_pretrained(model_path, trust_remote_code=True)
-
-image_path = "./figures/demo.png"
-image = Image.open(image_path)
-
-images_processed = processor(image, return_tensors="pt").to(dtype=model.dtype, device=model.device)
-image_features: list = model(images_processed.pixel_values, images_processed.image_grid_hws)
-
-print(f"dtype: {image_features[0].dtype}, shape: {image_features[0].shape}")
-# dtype: torch.bfloat16, shape: torch.Size([1092, 4, 1152])
+---
+license: mit
+pipeline_tag: image-feature-extraction
+library_name: transformers
+---
+
+<div align="center">
+  <img width="30%" src="figures/logo.png">
+</div>
+
+
+## Introduction
+
+**MoonViT** is a Native-resolution Vision Encoder, which is initialized from and continually pre-trained on **SigLIP-SO-400M**.
+To facilitate the standalone use of MoonViT, we have separated the implementation and weights of MoonViT from [moonshotai/Kimi-VL-A3B-Instruct](https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct).
+
+If you are interested in the training process of MoonViT, you are welcome to read Paper [Kimi-VL Technical Report](https://huggingface.co/papers/2504.07491).
+
+## Example usage
+
+```python
+from PIL import Image
+from transformers import AutoModel, AutoImageProcessor
+
+model_path = "moonshotai/MoonViT-SO-400M"
+model = AutoModel.from_pretrained(
+    model_path,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True,
+)
+processor = AutoImageProcessor.from_pretrained(model_path, trust_remote_code=True)
+
+image_path = "./figures/demo.png"
+image = Image.open(image_path)
+
+images_processed = processor(image, return_tensors="pt").to(dtype=model.dtype, device=model.device)
+image_features: list = model(images_processed.pixel_values, images_processed.image_grid_hws)
+
+print(f"dtype: {image_features[0].dtype}, shape: {image_features[0].shape}")
+# dtype: torch.bfloat16, shape: torch.Size([1092, 4, 1152])
 ```