Update pipeline tag and add project links

README.md

@@ -1,23 +1,25 @@
 ---
+language:
+- en
+- zh
 library_name: transformers
-pipeline_tag: image-text-to-text
 license: apache-2.0
+pipeline_tag: video-text-to-text
 tags:
-  - multimodal
-  - vision-language
-  - image-text-to-text
-  - video-text-to-text
-  - llava
-  - llava-onevision-2
-  - qwen3
-language:
-  - en
-  - zh
+- multimodal
+- vision-language
+- image-text-to-text
+- video-text-to-text
+- llava
+- llava-onevision-2
+- qwen3
 ---
 
 # LLaVA-OneVision-2-8B-Instruct
 
-A multimodal vision-language model that handles **single images, multi-image, and video** inputs, built on a Qwen3-8B language backbone with a OneVision-style vision encoder.
+[Paper](https://huggingface.co/papers/2605.25979) | [Project Page](https://evolvinglmms-lab.github.io/LLaVA-OneVision-2/) | [GitHub](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-2)
+
+LLaVA-OneVision-2 (LLaVA-OV-2) is a multimodal vision-language model that handles **single images, multi-image, and video** inputs, built on a Qwen3-8B language backbone with a OneVision-style vision encoder. Its key advance is codec-stream tokenization, which treats compressed video as a continuous bit-cost stream for efficient long-video understanding.
 
 The model is distributed as a HuggingFace `transformers` checkpoint with custom code (`trust_remote_code=True`).
 
@@ -145,17 +147,6 @@ out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
 print(processor.tokenizer.decode(out[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
 ```
 
-Defaults for the codec pipeline live in `preprocessor_config.json` under the
-`"codec"` key (`target_canvas=32`, `group_size=32`, `images_per_group=4`,
-`patch=14`, `min_group_frames=8`, `max_group_frames=64`); they can be
-overridden per call via `codec_config={...}`.
-
-**Short-video behaviour:** if the input video has fewer frames than
-`target_canvas` requires (or fewer than `min_group_frames`), a `UserWarning`
-is emitted and inference proceeds with however many canvases `cv-preinfer`
-can actually form. For very short clips, falling back to the frame-sampling
-backend is usually a better choice.
-
 ## Notes
 
 - The vision tower is a OneVision-style encoder; the language backbone is **Qwen3-8B**.
@@ -166,3 +157,21 @@ backend is usually a better choice.
 ## License
 
 Apache-2.0 (model weights and code in this repository). The Qwen3-8B base is subject to its own license — see [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
+
+## Citation
+
+```bibtex
+@inproceedings{LLaVA-OneVision-2,
+  title={LLaVA-OneVision-2},
+  author={llava-onevision contributors},
+  booktitle={arXiv},
+  year={2026}
+}
+
+@inproceedings{LLaVA-OneVision-1.5,
+  title={LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training},
+  author={An, Xiang and Xie, Yin and Yang, Kaicheng and Zhang, Wenkang and Zhao, Xiuwei and Cheng, Zheng and Wang, Yirui and Xu, Songcen and Chen, Changrui and Wu, Chunsheng and Tan, Huajie and Li, Chunyuan and Yang, Jing and Yu, Jie and Wang, Xiyao and Qin, Bin and Wang, Yumeng and Yan, Zizhen and Feng, Ziyong and Liu, Ziwei and Li, Bo and Deng, Jiankang},
+  booktitle={arXiv},
+  year={2025}
+ }
+```