Repository: huggingface/transformers
Pull request: [DOC] Add missing dates in model cards

Cc @stevhliu ;)
I'm building a space to display a visual timeline of model releases in Transformers, happy to discuss this more and how we could integrate it to the docs once I have something working!


Changed files:
filename: docs/source/en/model_doc/apertus.md
patch: @@ -13,6 +13,9 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 
 -->
+*This model was released on 2025-09-02 and added to Hugging Face Transformers on 2025-08-28.*
+
+# Apertus
 
 <div style="float: right;">
     <div class="flex flex-wrap space-x-1">
@@ -23,7 +26,7 @@ rendered properly in your Markdown viewer.
     </div>
 </div>
 
-# Apertus
+## Overview
 
 [Apertus](https://www.swiss-ai.org) is a family of large language models from the Swiss AI Initiative.
 

filename: docs/source/en/model_doc/florence2.md
patch: @@ -13,6 +13,9 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 
 -->
+*This model was released on 2024-06-16 and added to Hugging Face Transformers on 2025-08-20.*
+
+# Florence-2
 
 <div style="float: right;">
     <div class="flex flex-wrap space-x-1">
@@ -21,7 +24,7 @@ rendered properly in your Markdown viewer.
     </div>
 </div>
 
-# Florence-2
+## Overview
 
 [Florence-2](https://huggingface.co/papers/2311.06242) is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages the FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.
 

filename: docs/source/en/model_doc/nllb.md
patch: @@ -13,6 +13,9 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 
 -->
+*This model was released on 2022-07-11 and added to Hugging Face Transformers on 2022-07-18.*
+
+# NLLB
 
 <div style="float: right;">
     <div class="flex flex-wrap space-x-1">
@@ -22,18 +25,15 @@ rendered properly in your Markdown viewer.
     </div>
 </div>
 
-*This model was released on 2022-07-11 and added to Hugging Face Transformers on 2022-07-18.*
-
-
-# NLLB
+## Overview
 
 [NLLB: No Language Left Behind](https://huggingface.co/papers/2207.04672) is a multilingual translation model. It's trained on data using data mining techniques tailored for low-resource languages and supports over 200 languages. NLLB features a conditional compute architecture using a Sparsely Gated Mixture of Experts.
 
 
 You can find all the original NLLB checkpoints under the [AI at Meta](https://huggingface.co/facebook/models?search=nllb) organization.
 
 > [!TIP]
-> This model was contributed by [Lysandre](https://huggingface.co/lysandre).  
+> This model was contributed by [Lysandre](https://huggingface.co/lysandre).
 > Click on the NLLB models in the right sidebar for more examples of how to apply NLLB to different translation tasks.
 
 The example below demonstrates how to translate text with [`Pipeline`] or the [`AutoModel`] class.
@@ -120,17 +120,17 @@ visualizer("UN Chief says there is no military solution in Syria")
    >>> tokenizer("How was your day?").input_ids
    [256047, 13374, 1398, 4260, 4039, 248130, 2]
    ```
-   
+
    To revert to the legacy behavior, use the code example below.
-   
+
    ```python
    >>> from transformers import NllbTokenizer
 
    >>> tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M", legacy_behaviour=True)
    ```
-   
+
  - For non-English languages, specify the language's [BCP-47](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200) code with the `src_lang` keyword as shown below.
- 
+
  - See example below for a translation from Romanian to German.
     ```python
     >>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

filename: docs/source/en/model_doc/sam2.md
patch: @@ -13,6 +13,10 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 
 -->
+*This model was released on 2024-07-29 and added to Hugging Face Transformers on 2025-08-14.*
+
+# SAM2
+
 <div style="float: right;">
     <div class="flex flex-wrap space-x-1">
         <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
@@ -21,8 +25,6 @@ rendered properly in your Markdown viewer.
     </div>
 </div>
 
-# SAM2
-
 ## Overview
 
 SAM2 (Segment Anything Model 2) was proposed in [Segment Anything in Images and Videos](https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/) by Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dollár, Christoph Feichtenhofer.

filename: docs/source/en/model_doc/sam2_video.md
patch: @@ -13,6 +13,10 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 
 -->
+*This model was released on 2024-07-29 and added to Hugging Face Transformers on 2025-08-14.*
+
+# SAM2 Video
+
 <div style="float: right;">
     <div class="flex flex-wrap space-x-1">
         <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
@@ -21,8 +25,6 @@ rendered properly in your Markdown viewer.
     </div>
 </div>
 
-# SAM2 Video
-
 ## Overview
 
 SAM2 (Segment Anything Model 2) was proposed in [Segment Anything in Images and Videos](https://ai.meta.com/research/publications/sam-2-segment-anything-in-images-and-videos/) by Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dollár, Christoph Feichtenhofer.