Update README

README.md

@@ -11,7 +11,7 @@ tags:
 - pytorch
 ---
 # Introduction
-[[`AIMv2 Paper`](#)] [[`BibTeX`](#citation)]
+[[`AIMv2 Paper`](https://arxiv.org/abs/2411.14402)] [[`BibTeX`](#citation)]
 
 We introduce the AIMv2 family of vision models pre-trained with a multimodal autoregressive objective.
 AIMv2 pre-training is simple and straightforward to train and scale effectively. Some AIMv2 highlights include:
@@ -69,12 +69,14 @@ outputs = model(**inputs)
 ## Citation
 If you find our work useful, please consider citing us as:
 ```bibtex
-@misc{fini2024multimodal,
-  title         = {Multimodal Autoregressive Pre-training of Large Vision Encoders},
-  author        = {Enrico Fini and Mustafa Shukor and Xiujun Li and Philipp Dufter and Michal Klein and David Haldimann and Sai Aitharaju and Victor Guilherme Turrisi da Costa and Louis Béthune and Zhe Gan and Alexander T Toshev and Marcin Eichner and Moin Nabi and Yinfei Yang and Joshua M. Susskind and Alaaeldin El-Nouby},
-  year          = {2024},
-  archivePrefix = {arXiv},
-  primaryClass  = {cs.CV},
+@misc{fini2024multimodalautoregressivepretraininglarge,
+  author      = {Fini, Enrico and Shukor, Mustafa and Li, Xiujun and Dufter, Philipp and Klein, Michal and Haldimann, David and Aitharaju, Sai and da Costa, Victor Guilherme Turrisi and Béthune, Louis and Gan, Zhe and Toshev, Alexander T and Eichner, Marcin and Nabi, Moin and Yang, Yinfei and Susskind, Joshua M. and El-Nouby, Alaaeldin},
+  url         = {https://arxiv.org/abs/2411.14402},
+  eprint      = {2411.14402},
+  eprintclass = {cs.CV},
+  eprinttype  = {arXiv},
+  title       = {Multimodal Autoregressive Pre-training of Large Vision Encoders},
+  year        = {2024},
 }
 ```
 

modeling_aimv2.py

@@ -102,7 +102,7 @@ class AIMv2ViTPreprocessor(nn.Module):
         pos_embed = get_sincos_pos_embed(
             H // self.patch_h, W // self.patch_w, embed_dim=self.embed_dim
         )
-        tokens = tokens + pos_embed.to(tokens.device)
+        tokens = tokens + pos_embed
         return tokens