[Docs] Add resources (huggingface#28705)

* Add resource

* Add more resources

* Add resources

* Apply suggestions from code review

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Remove mention

* Remove pipeline tags

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

docs/source/en/model_doc/depth_anything.md

@@ -94,6 +94,15 @@ If you want to do the pre- and postprocessing yourself, here's how to do that:
 >>> depth = Image.fromarray(formatted)
 ```
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Depth Anything.
+
+- [Monocular depth estimation task guide](../tasks/depth_estimation)
+- A notebook showcasing inference with [`DepthAnythingForDepthEstimation`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Depth%20Anything/Predicting_depth_in_an_image_with_Depth_Anything.ipynb). 🌎
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## DepthAnythingConfig
 
 [[autodoc]] DepthAnythingConfig

docs/source/en/model_doc/patchtsmixer.md

@@ -28,14 +28,14 @@ The abstract from the paper is the following:
 
 *TSMixer is a lightweight neural architecture exclusively composed of multi-layer perceptron (MLP) modules designed for multivariate forecasting and representation learning on patched time series. Our model draws inspiration from the success of MLP-Mixer models in computer vision. We demonstrate the challenges involved in adapting Vision MLP-Mixer for time series and introduce empirically validated components to enhance accuracy. This includes a novel design paradigm of attaching online reconciliation heads to the MLP-Mixer backbone, for explicitly modeling the time-series properties such as hierarchy and channel-correlations. We also propose a Hybrid channel modeling approach to effectively handle noisy channel interactions and generalization across diverse datasets, a common challenge in existing patch channel-mixing methods. Additionally, a simple gated attention mechanism is introduced in the backbone to prioritize important features. By incorporating these lightweight components, we significantly enhance the learning capability of simple MLP structures, outperforming complex Transformer models with minimal computing usage. Moreover, TSMixer's modular design enables compatibility with both supervised and masked self-supervised learning methods, making it a promising building block for time-series Foundation Models. TSMixer outperforms state-of-the-art MLP and Transformer models in forecasting by a considerable margin of 8-60%. It also outperforms the latest strong benchmarks of Patch-Transformer models (by 1-2%) with a significant reduction in memory and runtime (2-3X).*
 
-
-
 This model was contributed by [ajati](https://huggingface.co/ajati), [vijaye12](https://huggingface.co/vijaye12), 
 [gsinthong](https://huggingface.co/gsinthong), [namctin](https://huggingface.co/namctin),
 [wmgifford](https://huggingface.co/wmgifford), [kashif](https://huggingface.co/kashif).
 
+## Usage example
+
+The code snippet below shows how to randomly initialize a PatchTSMixer model. The model is compatible with the [Trainer API](../trainer.md).
 
-## Sample usage 
 ```python
 
 from transformers import PatchTSMixerConfig, PatchTSMixerForPrediction
@@ -55,6 +55,10 @@ results = trainer.evaluate(test_dataset)
 
 The model can also be used for time series classification and time series regression. See the respective [`PatchTSMixerForTimeSeriesClassification`] and [`PatchTSMixerForRegression`] classes.
 
+## Resources
+
+- A blog post explaining PatchTSMixer in depth can be found [here](https://huggingface.co/blog/patchtsmixer). The blog can also be opened in Google Colab.
+
 ## PatchTSMixerConfig
 
 [[autodoc]] PatchTSMixerConfig

docs/source/en/model_doc/patchtst.md

@@ -34,6 +34,9 @@ This model was contributed by [namctin](https://huggingface.co/namctin), [gsinth
 
 The model can also be used for time series classification and time series regression. See the respective [`PatchTSTForClassification`] and [`PatchTSTForRegression`] classes.
 
+## Resources
+
+- A blog post explaining PatchTST in depth can be found [here](https://huggingface.co/blog/patchtst). The blog can also be opened in Google Colab.
 
 ## PatchTSTConfig
 

docs/source/en/model_doc/sam.md

@@ -94,12 +94,20 @@ masks = processor.image_processor.post_process_masks(
 scores = outputs.iou_scores
 ```
 
-Resources:
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SAM.
 
 - [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/segment_anything.ipynb) for using the model.
 - [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/automatic_mask_generation.ipynb) for using the automatic mask generation pipeline.
-- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Run_inference_with_MedSAM_using_HuggingFace_Transformers.ipynb) for inference with MedSAM, a fine-tuned version of SAM on the medical domain.
-- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Fine_tune_SAM_(segment_anything)_on_a_custom_dataset.ipynb) for fine-tuning the model on custom data.
+- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Run_inference_with_MedSAM_using_HuggingFace_Transformers.ipynb) for inference with MedSAM, a fine-tuned version of SAM on the medical domain. 🌎
+- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Fine_tune_SAM_(segment_anything)_on_a_custom_dataset.ipynb) for fine-tuning the model on custom data. 🌎
+
+## SlimSAM
+
+SlimSAM, a pruned version of SAM, was proposed in [0.1% Data Makes Segment Anything Slim](https://arxiv.org/abs/2312.05284) by Zigeng Chen et al. SlimSAM reduces the size of the SAM models considerably while maintaining the same performance.
+
+Checkpoints can be found on the [hub](https://huggingface.co/models?other=slimsam), and they can be used as a drop-in replacement of SAM.
 
 ## SamConfig
 

docs/source/en/model_doc/siglip.md

@@ -94,6 +94,15 @@ If you want to do the pre- and postprocessing yourself, here's how to do that:
 31.9% that image 0 is 'a photo of 2 cats'
 ```
 
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SigLIP.
+
+- [Zero-shot image classification task guide](../tasks/zero_shot_image_classification_md)
+- Demo notebooks for SigLIP can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SigLIP). 🌎
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
 ## SiglipConfig
 
 [[autodoc]] SiglipConfig

docs/source/en/model_doc/whisper.md

@@ -31,7 +31,6 @@ The original code can be found [here](https://github.com/openai/whisper).
 
 - The model usually performs well without requiring any finetuning.
 - The architecture follows a classic encoder-decoder architecture, which means that it relies on the [`~generation.GenerationMixin.generate`] function for inference.
-- Inference is currently only implemented for short-form i.e. audio is pre-segmented into <=30s segments. Long-form (including timestamps) will be implemented in a future release.
 - One can use [`WhisperProcessor`] to prepare audio for the model, and decode the predicted ID's back into text.
 
 - To convert the model and the processor, we recommend using the following: