Add pipeline_tag and library_name to metadata

Hi,

I'm Niels from the Hugging Face community science team. This PR improves the model card's metadata by adding the `pipeline_tag` and `library_name`.

Specifically:
- `pipeline_tag: video-text-to-text` ensures the model is correctly categorized under video-to-text tasks on the Hub.
- `library_name: transformers` acknowledges compatibility with the Transformers library (using `trust_remote_code=True`) and enables the "Use in Transformers" button on the repository.

These changes help improve the discoverability and usability of the model.

Best,
Niels

README.md

@@ -1,5 +1,7 @@
 ---
 license: cc-by-nc-4.0
+library_name: transformers
+pipeline_tag: video-text-to-text
 tags:
 - AutoGaze
 - NVILA
@@ -25,11 +27,16 @@ from transformers import AutoModel, AutoProcessor
 
 model_path = "nvidia/NVILA-8B-HD-Video"
 video_path = "https://huggingface.co/datasets/bfshi/HLVid/resolve/main/example/clip_av_video_5_001.mp4"
-prompt = "Question: What does the white text on the green road sign say?\n \
-A. Hampden St\n \
-B. Hampden Ave\n \
-C. HampdenBlvd\n \
-D. Hampden Rd\n \
+prompt = "Question: What does the white text on the green road sign say?
+ \
+A. Hampden St
+ \
+B. Hampden Ave
+ \
+C. HampdenBlvd
+ \
+D. Hampden Rd
+ \
 Please answer directly with the letter of the correct answer."
 
 # ----- Video processing args -----
@@ -73,7 +80,9 @@ model.eval()
 
 # Run inference
 video_token = processor.tokenizer.video_token
-inputs = processor(text=f"{video_token}\n\n{prompt}", videos=video_path, return_tensors="pt")
+inputs = processor(text=f"{video_token}
+
+{prompt}", videos=video_path, return_tensors="pt")
 inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
 
 outputs = model.generate(**inputs)
@@ -173,4 +182,4 @@ The model is tested on NVIDIA A100 GPU.
 
 
 ### Ethical Considerations:
-NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).