Add model card with metadata and usage example

README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: image-text-to-text
+---
+
+# Sky-VLM: Is your VLM Sky-Ready? A Comprehensive Spatial Intelligence Benchmark for UAV Navigation
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-9BDFDF)](https://github.com/linglingxiansen/SpatialSky/blob/main/LICENSE)
+[![hf_checkpoint](https://img.shields.io/badge/🤗-Checkpoint-FBD49F.svg)](https://huggingface.co/llxs/Sky-VLM)
+[![arXiv](https://img.shields.io/badge/Arxiv-2511.13269-E69191.svg?logo=arXiv)](https://arxiv.org/abs/2511.13269)
+
+This repository hosts the **Sky-VLM** model, a specialized Vision-Language Model designed for UAV spatial reasoning across multiple granularities and contexts. It was introduced in the paper [Is your VLM Sky-Ready? A Comprehensive Spatial Intelligence Benchmark for UAV Navigation](https://huggingface.co/papers/2511.13269).
+
+The project's code is available on GitHub: [https://github.com/linglingxiansen/SpatialSKy](https://github.com/linglingxiansen/SpatialSKy).
+
+## 🚀 Sample Usage
+
+First, install the `transformers` library and other dependencies as described in the [GitHub repository](https://github.com/linglingxiansen/SpatialSky#installation):
+```bash
+pip install git+https://github.com/huggingface/transformers accelerate torch torchvision openai pillow tqdm nltk scipy
+```
+
+Then, you can use the following Python code for inference with the `Sky-VLM` model:
+
+```python
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info # Note: qwen_vl_utils might need to be installed separately or adapted
+
+# Default: Load the model on the available device(s)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "llxs/Sky-VLM", torch_dtype="auto", device_map="auto"
+)
+processor = AutoProcessor.from_pretrained("llxs/Sky-VLM")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "./examples/images/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png", # Placeholder image path
+            },
+            {"type": "text", "text": "In this UI screenshot, what is the position of the element corresponding to the command \\\"switch language of current page\\\" (with bbox)?"},
+        ],
+    }
+]
+
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+# Assuming process_vision_info is available from qwen_vl_utils or a similar helper
+# For a minimal example, image_inputs can be directly a list of PIL Images or similar
+# If qwen_vl_utils is not installed, manual processing might be needed.
+# For simplicity, if this exact helper isn't critical for basic HF inference, we might skip/adapt.
+# Here, we assume its presence for direct copy.
+image_inputs, video_inputs = process_vision_info(messages) # Requires qwen_vl_utils for this exact function
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+
+generated_ids_trimmed = [
+    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
+)
+print(output_text)
+# Expected output example: <|object_ref_start|>language switch<|object_ref_end|><|box_start|>(576,12),(592,42)<|box_end|><|im_end|>
+```