Correct pipeline tag, add link to paper

Correcting the pipeline tag and linking it to the paper, ensuring people can find your model at https://huggingface.co/models?pipeline_tag=video-text-to-text.

README.md

@@ -1,6 +1,6 @@
 ---
-library_name: transformers
-license: apache-2.0
+base_model:
+- HuggingFaceTB/SmolVLM-256M-Instruct
 datasets:
 - HuggingFaceM4/the_cauldron
 - HuggingFaceM4/Docmatix
@@ -14,17 +14,19 @@ datasets:
 - TIGER-Lab/VISTA-400K
 - Enxin/MovieChat-1K_train
 - ShareGPT4Video/ShareGPT4Video
-pipeline_tag: image-text-to-text
 language:
 - en
-base_model:
-- HuggingFaceTB/SmolVLM-256M-Instruct
+library_name: transformers
+license: apache-2.0
+pipeline_tag: video-text-to-text
 ---
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/SmolVLM2_banner.png" width="800" height="auto" alt="Image description">
 
 # SmolVLM2-256M-Video
 
+This repository contains the model as presented in [SmolVLM: Redefining small and efficient multimodal models](https://huggingface.co/papers/2504.05299).
+
 SmolVLM2-256M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.38GB of GPU RAM for video inference. This efficiency makes it particularly well-suited for on-device applications that require specific domain fine-tuning and computational resources may be limited.
 ## Model Summary
 
@@ -207,12 +209,7 @@ You can cite us in the following way:
 ## Training Data
 SmolVLM2 used 3.3M samples for training originally from ten different datasets: [LlaVa Onevision](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), [M4-Instruct](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data), [Mammoth](https://huggingface.co/datasets/MAmmoTH-VL/MAmmoTH-VL-Instruct-12M), [LlaVa Video 178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K), [FineVideo](https://huggingface.co/datasets/HuggingFaceFV/finevideo), [VideoStar](https://huggingface.co/datasets/orrzohar/Video-STaR), [VRipt](https://huggingface.co/datasets/Mutonix/Vript), [Vista-400K](https://huggingface.co/datasets/TIGER-Lab/VISTA-400K), [MovieChat](https://huggingface.co/datasets/Enxin/MovieChat-1K_train) and [ShareGPT4Video](https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video).
 In the following plots we give a general overview of the samples across modalities and the source of those samples.
-<!--
-<center><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_data_split.png" width="auto" height="auto" alt="Image description">
-</center>
 
-### Details
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_datadetails.png" width="auto" height="auto" alt="Image description"> -->
 
 ## Data Split per modality
 
@@ -266,4 +263,4 @@ In the following plots we give a general overview of the samples across modaliti
 | video-star/starb                           | 2.2%       |
 | vista-400k/combined                        | 2.2%       |
 | vript/long                                 | 1.0%       |
-| ShareGPT4Video/all                         | 0.8%       |
+| ShareGPT4Video/all                         | 0.8%       |