diff --git a/.github/workflows/rust-ci.yml b/.github/workflows/rust-ci.yml
index ee480c474c..db25503079 100644
--- a/.github/workflows/rust-ci.yml
+++ b/.github/workflows/rust-ci.yml
@@ -16,6 +16,9 @@ jobs:
         rust: [stable]
     steps:
       - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
       - uses: actions-rs/toolchain@v1
         with:
           profile: minimal
@@ -35,6 +38,9 @@ jobs:
         rust: [stable]
     steps:
       - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
       - uses: actions-rs/toolchain@v1
         with:
           profile: minimal
diff --git a/candle-transformers/src/models/bert.rs b/candle-transformers/src/models/bert.rs
index 808ca41557..da8734160a 100644
--- a/candle-transformers/src/models/bert.rs
+++ b/candle-transformers/src/models/bert.rs
@@ -7,56 +7,6 @@
 //! - Upstream [Github repo](https://github.com/google-research/bert).
 //! - See bert in [candle-examples](https://github.com/huggingface/candle/tree/main/candle-examples/) for runnable code
 //!
-//! ```no_run
-//! // for sentence embeddings
-//! # use candle_core::Tensor;
-//! # use candle_nn::{VarBuilder, Module};
-//! # fn main() -> candle_core::Result<()> {
-//! # let model = todo!();
-//! # let prompt = "Here is a test sentence";
-//! let embeddings = model.forward(prompt)?;
-//! // Returns tensor of shape [1, 7, 384]
-//! println!("{embeddings}");
-//! # Ok(())
-//! # }
-//!
-//! // Different models can be loaded using the model ID
-//! # use candle_core::Tensor;
-//! # use candle_nn::{VarBuilder, Module};
-//! # fn main() -> candle_core::Result<()> {
-//! # let vb = todo!();
-//! # let config = todo!();
-//! let model = BertModel::load(vb, &config )?;
-//! # Ok(())
-//! # }
-//!
-//! // Gelu approximation
-//! // You can get a speedup by configuring the model
-//! // to use an approximation of the gelu activation:
-//! # use candle_core::Tensor;
-//! # use candle_nn::{VarBuilder, Module};
-//! # fn main() -> candle_core::Result<()> {
-//! # let mut config = todo!();
-//! config.hidden_act = HiddenAct::GeluApproximate;
-//! # Ok(())
-//! # }
-//!
-//! // Similarities
-//! // Bert can compute sentence embeddings which can then be used to calculate
-//! // semantic similarities between sentences through cosine similarity scoring.
-//! // The sentence embeddings are computed using average pooling across all tokens.
-//! # use candle_core::Tensor;
-//! # use candle_nn::{VarBuilder, Module};
-//! # fn main() -> candle_core::Result<()> {
-//! # let model = todo!();
-//! let sentence1 = "The new movie is awesome";
-//! let sentence2 = "The new movie is so great";
-//! let emb1 = model.forward(sentence1)?;
-//! let emb2 = model.forward(sentence2)?;
-//! # Ok(())
-//! # }
-//! ```
-//!
 use super::with_tracing::{layer_norm, linear, LayerNorm, Linear};
 use candle::{DType, Device, Result, Tensor};
 use candle_nn::{embedding, Embedding, Module, VarBuilder};
diff --git a/candle-transformers/src/models/dinov2.rs b/candle-transformers/src/models/dinov2.rs
index df8834d1f7..4d46941f8b 100644
--- a/candle-transformers/src/models/dinov2.rs
+++ b/candle-transformers/src/models/dinov2.rs
@@ -1,8 +1,42 @@
 //! Implementation of the DINOv2 models from Meta Research.
 //!
-//! See:
-//! - DINOv2: ["DINOv2: Learning Robust Visual Features without Supervision"](https://github.com/facebookresearch/dinov2)
+//! This module implements the DINOv2 vision transformer model from Meta AI Research.
+//! DINOv2 is a self-supervised learning model that can learn visual features
+//! without using any labeled data. See: ["DINOv2: Learning Robust Visual Features without Supervision"](https://github.com/facebookresearch/dinov2)
 //!
+//! ## Running an example with color map and CUDA
+//!
+//! ```bash
+//! cargo run \
+//!   --features cuda,depth_anything_v2 \
+//!   --package candle-examples \
+//!   --example depth_anything_v2 \
+//!   -- --color-map \
+//!   --image candle-examples/examples/yolo-v8/assets/bike.jpg
+//! ```
+//!
+//! ## Running as an ImageNet classifier
+//!
+//! The model returns the probability for the image to belong to each of the 1000 ImageNet categories.
+//!
+//! <div align=center>
+//!   <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.jpg" alt="" width=640>
+//! </div>
+//!
+//! ```bash
+//! cargo run \
+//!   --example dinov2 \
+//!   --release \
+//!   -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
+//!
+//! > mountain bike, all-terrain bike, off-roader: 43.67%
+//! > bicycle-built-for-two, tandem bicycle, tandem: 33.20%
+//! > crash helmet            : 13.23%
+//! > unicycle, monocycle     : 2.44%
+//! > maillot                 : 2.42%
+//! ```
+//!
+
 use candle::{IndexOp, Result, Tensor, D};
 use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
 
diff --git a/candle-transformers/src/models/dinov2reg4.rs b/candle-transformers/src/models/dinov2reg4.rs
index 0d2320e14c..549f2c3ce5 100644
--- a/candle-transformers/src/models/dinov2reg4.rs
+++ b/candle-transformers/src/models/dinov2reg4.rs
@@ -1,9 +1,34 @@
 //! Implementation of the DINOv2 revision (4 regularization)
 //!
-//! See:
-//! - DINOv2: ["DINOv2: Learning Robust Visual Features without Supervision"](https://github.com/facebookresearch/dinov2)
+//! The DINOv2-reg4 model is a variant of DINOv2 that adds 4 regularization tokens to the
+//! original architecture. This implementation is specifically trained for plant species
+//! classification on the PlantCLEF2024 dataset with 7,806 classes.
 //!
-//! This code implements the regularization tokens version with 4 regularization tokens.
+//! - [Paper](https://arxiv.org/abs/2309.16588). DINOv2: Learning Robust Visual Features without Supervision
+//! - [GH Repo](https://github.com/facebookresearch/dinov2)
+//!
+//! # Example
+//!
+//! ```bash
+//! # Download classes names and a plant picture to identify
+//! # see candle/examples/dinov2reg4 for full code.
+//!
+//! # Perform inference
+//! cargo run \
+//!   --example dinov2reg4 \
+//!   --release -- \
+//!   --image <orchid-file>
+//!
+//! > Orchis simia Lam.       : 45.55%
+//! > Orchis × bergonii Nanteuil: 9.80%
+//! > Orchis italica Poir.    : 9.66%
+//! > Orchis × angusticruris Franch.: 2.76%
+//! > Orchis × bivonae Tod.   : 2.54%
+//! ```
+//!
+//! <div align=center>
+//!   <img src="https://bs.plantnet.org/image/o/bd2d3830ac3270218ba82fd24e2290becd01317c" alt="" width=320>
+//! </div>
 //!
 use candle::{IndexOp, Result, Tensor, D};
 use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
diff --git a/candle-transformers/src/models/efficientvit.rs b/candle-transformers/src/models/efficientvit.rs
index 9724f702a6..4c231d7679 100644
--- a/candle-transformers/src/models/efficientvit.rs
+++ b/candle-transformers/src/models/efficientvit.rs
@@ -1,9 +1,40 @@
 //! EfficientViT (MSRA) inference implementation based on timm.
 //!
-//! See ["EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention"](https://arxiv.org/abs/2305.07027)
+//! This crate provides an implementation of the EfficientViT model from Microsoft Research Asia
+//! for efficient image classification. The model uses cascaded group attention modules
+//! to achieve strong performance while maintaining low memory usage.
+//!
+//! The model was originally described in the paper:
+//! ["EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention"](https://arxiv.org/abs/2305.07027)
+//!
+//! This implementation is based on the reference implementation from
+//! [pytorch-image-models](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/efficientvit_msra.py).
+//!
+//! # Example Usage
+//!
+//! This candle implementation uses a pre-trained EfficientViT (from Microsoft Research Asia) network for inference.
+//! The classification head has been trained on the ImageNet dataset and returns the probabilities for the top-5 classes.
+//!
+//!
+//! ```bash
+//! cargo run
+//!   --example efficientvit \
+//!   --release -- \
+//!   --image candle-examples/examples/yolo-v8/assets/bike.jpg --which m1
+//!
+//! > loaded image Tensor[dims 3, 224, 224; f32]
+//! > model built
+//! > mountain bike, all-terrain bike, off-roader: 69.80%
+//! > unicycle, monocycle     : 13.03%
+//! > bicycle-built-for-two, tandem bicycle, tandem: 9.28%
+//! > crash helmet            : 2.25%
+//! > alp                     : 0.46%
+//! ```
+//!
+//! <div align=center>
+//!   <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.jpg" alt="" width=640>
+//! </div>
 //!
-//! Based on implementation from [pytorch-image-models](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/efficientvit_msra.py)
-
 use candle::{Result, Tensor, D};
 use candle_nn::{
     batch_norm, conv2d, conv2d_no_bias, linear, ops::sigmoid, ops::softmax, Conv2dConfig, Func,
diff --git a/candle-transformers/src/models/eva2.rs b/candle-transformers/src/models/eva2.rs
index ee84cca43c..9e31f58c73 100644
--- a/candle-transformers/src/models/eva2.rs
+++ b/candle-transformers/src/models/eva2.rs
@@ -1,9 +1,31 @@
 //! EVA-2 inference implementation.
 //!
-//! See ["EVA-02: A Visual Representation for Neon Genesis"](https://arxiv.org/abs/2303.11331)
+//! EVA-02 is a computer vision model that can be used as an ImageNet classifier.
+//! The model returns the probability for an image to belong to each of the 1000
+//! ImageNet categories.
+//!
+//! - [Paper](https://arxiv.org/abs/2303.11331). EVA-02: A Visual Representation for Neon Genesis
+//! - [Code](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/eva2.py)
+//!
+//! # Example
+//!
+//! ```bash
+//! cargo run \
+//!   --example eva2 \
+//!   --release -- \
+//!   --image candle-examples/examples/yolo-v8/assets/bike.jpg
+//!
+//! > mountain bike, all-terrain bike, off-roader: 37.09%
+//! > maillot                 : 8.30%
+//! > alp                     : 2.13%
+//! > bicycle-built-for-two, tandem bicycle, tandem: 0.84%
+//! > crash helmet            : 0.73%
+//! ```
+//!
+//! <div align=center>
+//!   <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.jpg" alt="" width=640>
+//! </div>
 //!
-//! Based on implementation from [pytorch-image-models](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/eva2.py)
-
 use candle::{IndexOp, Result, Tensor, D};
 use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
 
diff --git a/candle-transformers/src/models/moondream.rs b/candle-transformers/src/models/moondream.rs
index d351d7c019..a9dc9b7dc2 100644
--- a/candle-transformers/src/models/moondream.rs
+++ b/candle-transformers/src/models/moondream.rs
@@ -1,13 +1,39 @@
 //! MoonDream Model vision-to-text
 //!
+//!
+//! Moondream is a computer-vision model that can answer real-world questions about images.
+//! It's lightweight with only 1.6B parameters, enabling it to run on mobile phones and edge devices.
+//! [MoonDream Original Implementation](https://github.com/vikhyat/moondream)
+//!
 //! The model consists of:
 //! - Vision encoder using a ViT-style architecture
 //! - Text decoder based on Microsoft's Phi model
 //! - Vision projection module to align vision and text embeddings
 //!
-//! References:
-//! - [MoonDream Original Implementation](https://github.com/vikhyat/moondream)
+//! # Examples
+//!
+//! <img src="https://raw.githubusercontent.com/vikhyat/moondream/main/assets/demo-1.jpg" width="200">
+//!
+//! ```bash
+//! # download an example image
+//! wget https://raw.githubusercontent.com/vikhyat/moondream/main/assets/demo-1.jpg
+//!
+//! # Now you can run Moondream from the `candle-examples` crate:
+//! cargo run --example moondream \
+//!   --release -- \
+//!   --prompt "What is the girl eating?"
+//!   --image "./demo-1.jpg"
 //!
+//! > avavx: false, neon: true, simd128: false, f16c: false
+//! > temp: 0.00 repeat-penalty: 1.00 repeat-last-n: 64
+//! > retrieved the files in 3.395583ms
+//! > Running on CPU, to run on GPU(metal), build this example with `--features metal`
+//! > loaded the model in 5.485493792s
+//! > loaded and encoded the image Tensor[dims 3, 378, 378; f32] in 4.801396417s
+//! > starting the inference loop
+//! > The girl is eating a hamburger.<
+//! > 9 tokens generated (0.68 token/s)
+//! ```
 
 use crate::models::mixformer::{Config as PhiConfig, MixFormerSequentialForCausalLM as PhiModel};
 use crate::models::with_tracing::{layer_norm, linear_b, LayerNorm, Linear};
diff --git a/candle-transformers/src/models/rwkv_v5.rs b/candle-transformers/src/models/rwkv_v5.rs
index 6390f886d2..15e386d292 100644
--- a/candle-transformers/src/models/rwkv_v5.rs
+++ b/candle-transformers/src/models/rwkv_v5.rs
@@ -1,7 +1,9 @@
 //! RWKV v5 model implementation.
 //!
-//! RWKV is an RNN with transformer-level performance that can be implemented
-//! as either a transformer or RNN.
+//! The [RWKV model](https://wiki.rwkv.com/) is a recurrent neural network model
+//! with performance on par with transformer architectures. Several variants are
+//! available, candle implements the v5 and v6 versions and can be used with
+//! Eagle 7B([blog post](https://blog.rwkv.com/p/eagle-7b-soaring-past-transformers)).
 //!
 //! Key characteristics:
 //! - Time-mix attention mechanism
@@ -14,6 +16,20 @@
 //! - [RWKV Language Model](https://github.com/BlinkDL/RWKV-LM)
 //! - [RWKV v5 Release](https://github.com/BlinkDL/ChatRWKV/tree/main)
 //!
+//! # Example
+//!
+//! ```bash
+//! cargo run --example rwkv --release -- \
+//!   --prompt "The smallest prime is "
+//!
+//! > avx: true, neon: false, simd128: false, f16c: true
+//! > temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
+//! > The smallest prime is ϕ(2) = 2.
+//! > The smallest composite is ϕ(3) = 3.
+//! > The smallest perfect number is ϕ(5) = 5.
+//! > The smallest perfect square is ϕ(4) = 4.
+//! > The smallest perfect cube is ϕ(6) = 6.
+//! ```
 
 use super::with_tracing::{layer_norm, linear_no_bias as linear, LayerNorm, Linear};
 use candle::{DType, Device, IndexOp, Result, Tensor};
diff --git a/candle-transformers/src/models/rwkv_v6.rs b/candle-transformers/src/models/rwkv_v6.rs
index c75aa885e9..5da1c5ce81 100644
--- a/candle-transformers/src/models/rwkv_v6.rs
+++ b/candle-transformers/src/models/rwkv_v6.rs
@@ -1,7 +1,9 @@
 //! RWKV v6 model implementation.
 //!
-//! RWKV is an RNN with transformer-like performance.
-//! Version 6 introduces refinements to the architecture.
+//! The [RWKV model](https://wiki.rwkv.com/) is a recurrent neural network model
+//! with performance on par with transformer architectures. Several variants are
+//! available, candle implements the v5 and v6 versions and can be used with
+//! Eagle 7B([blog post](https://blog.rwkv.com/p/eagle-7b-soaring-past-transformers)).
 //!
 //! Key characteristics:
 //! - Linear attention mechanism
@@ -10,9 +12,20 @@
 //! - Feed forward gating
 //! - State recycling for efficient inference
 //!
-//! References:
-//! - [RWKV Model](https://github.com/BlinkDL/RWKV-LM)
+//! # Example
 //!
+//! ```bash
+//! cargo run --example rwkv --release -- \
+//!   --prompt "The smallest prime is "
+//!
+//! > avx: true, neon: false, simd128: false, f16c: true
+//! > temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
+//! > The smallest prime is ϕ(2) = 2.
+//! > The smallest composite is ϕ(3) = 3.
+//! > The smallest perfect number is ϕ(5) = 5.
+//! > The smallest perfect square is ϕ(4) = 4.
+//! > The smallest perfect cube is ϕ(6) = 6.
+//! ```
 
 use super::with_tracing::{layer_norm, linear_no_bias as linear, LayerNorm, Linear};
 use candle::{IndexOp, Result, Tensor};
diff --git a/candle-transformers/src/models/segment_anything/mod.rs b/candle-transformers/src/models/segment_anything/mod.rs
index 3e85fe3594..fe0b099008 100644
--- a/candle-transformers/src/models/segment_anything/mod.rs
+++ b/candle-transformers/src/models/segment_anything/mod.rs
@@ -1,10 +1,33 @@
 //! Segment Anything Model (SAM)
 //!
 //! SAM is an architecture for image segmentation, capable of segmenting any object
-//! in an image based on prompts like points or boxes.
+//! in an image based on prompts like points or boxes. //! This model provides a robust and fast image segmentation pipeline that can be tweaked via
+//! some prompting (requesting some points to be in the target mask, requesting some
+//! points to be part of the background so _not_ in the target mask, specifying some
+//! bounding box).
 //!
-//! - [GH Link](https://github.com/facebookresearch/segment-anything)
-//! - [Paper](https://arxiv.org/abs/2304.02643)
+//! - ⚡ [Interactive Wasm Example](https://huggingface.co/spaces/radames/candle-segment-anything-wasm)
+//! - 💻 [GH Link](https://github.com/facebookresearch/segment-anything)
+//! - 📝 [Paper](https://arxiv.org/abs/2304.02643)
+//! - 💡 The default backbone can be replaced by the smaller and faster TinyViT model based on [MobileSAM](https://github.com/ChaoningZhang/MobileSAM).
+//!
+//!
+//! ## Example
+//!
+//! ```bash
+//! cargo run --example segment-anything --release -- \
+//!     --image candle-examples/examples/yolo-v8/assets/bike.jpg
+//!     --use-tiny --point 0.6,0.6 --point 0.6,0.55
+//! ```
+//!
+//! <div align=center style="display: flex; justify-content: center; gap: 10px;">
+//!   <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.jpg" alt="" width="30%">
+//!   <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/segment-anything/assets/single_pt_prompt.jpg" alt="" width="30%">
+//!   <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/segment-anything/assets/two_pt_prompt.jpg" alt="" width="30%">
+//! </div>
+//!
+//!
+//! > Original; Prompt with `--point 0.6,0.55`; Prompt with `--point 0.6,0.6 --point 0.6,0.55`
 //!
 pub use crate::models::with_tracing::Linear;
 use candle::{Result, Tensor};
diff --git a/candle-transformers/src/models/stable_diffusion/mod.rs b/candle-transformers/src/models/stable_diffusion/mod.rs
index d3e2032b6e..458a7de2d4 100644
--- a/candle-transformers/src/models/stable_diffusion/mod.rs
+++ b/candle-transformers/src/models/stable_diffusion/mod.rs
@@ -5,7 +5,37 @@
 //!
 //! - [Original Repository](https://github.com/CompVis/stable-diffusion)
 //! - [Hugging Face](https://huggingface.co/runwayml/stable-diffusion-v1-5)
+//! -  The default scheduler for the v1.5, v2.1 and XL 1.0 version is the Denoising Diffusion Implicit Model scheduler (DDIM). The original paper and some code can be found in the [associated repo](https://github.com/ermongroup/ddim). The default scheduler for the XL Turbo version is the Euler Ancestral scheduler.
 //!
+//!
+//! # Example
+//!
+//! <div align=center>
+//!   <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg" alt="rusty robot holding a candle" width=320>
+//! </div>
+//!
+//! _"A rusty robot holding a fire torch in its hand."_ Generated by Stable Diffusion XL using Rust and [candle](https://github.com/huggingface/candle).
+//!
+//! ```bash
+//! # example running with cuda
+//! # see the candle-examples/examples/stable-diffusion for all options
+//! cargo run --example stable-diffusion --release --features=cuda,cudnn \
+//!     -- --prompt "a cosmonaut on a horse (hd, realistic, high-def)"
+//!
+//! # with sd-turbo
+//! cargo run --example stable-diffusion --release --features=cuda,cudnn \
+//!     -- --prompt "a cosmonaut on a horse (hd, realistic, high-def)" \
+//!     --sd-version turbo
+//!
+//! # with flash attention.
+//! # feature flag: `--features flash-attn`
+//! # cli flag: `--use-flash-attn`.
+//! # flash-attention-v2 is only compatible with Ampere, Ada, \
+//! # or Hopper GPUs (e.g., A100/H100, RTX 3090/4090).
+//! cargo run --example stable-diffusion --release --features=cuda,cudnn \
+//!     -- --prompt "a cosmonaut on a horse (hd, realistic, high-def)" \
+//!     --use-flash-attn
+//! ```
 
 pub mod attention;
 pub mod clip;
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index 9da0c1afec..d3fd2ba686 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -14,6 +14,49 @@
 //! - [T5 Paper](https://arxiv.org/abs/1910.10683)
 //! - [HuggingFace T5](https://huggingface.co/docs/transformers/model_doc/t5)
 //! - [GH Model](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py)
+//!
+//! # Encoder-decoder example:
+//!
+//! ```bash
+//! cargo run --example t5 --release -- \
+//!   --model-id "t5-small" \
+//!   --prompt "translate to German: A beautiful candle." \
+//!   --decode
+//! > ...
+//! >  Eine schöne Kerze.
+//! > 9 tokens generated (2.42 token/s)
+//! ```
+//!
+//! Variants such as [flan-t5](https://huggingface.co/google/flan-t5-small), [flan-ul2](https://huggingface.co/google/flan-ul2) (with `--revision "refs/pr/25"`), and [Co-EdIT](https://huggingface.co/grammarly/coedit-large) are also supported.
+//!
+//! # Translation with MADLAD
+//!
+//!
+//! [MADLAD-400](https://arxiv.org/abs/2309.04662) is a series of multilingual machine translation T5 models trained on 250 billion tokens covering over 450 languages using publicly available data. These models are competitive with significantly larger models.
+//!
+//! ```bash
+//! cargo run --example t5 --release  -- \
+//!   --model-id "jbochi/madlad400-3b-mt" \
+//!   --prompt "<2de> How are you, my friend?" \
+//!   --decode --temperature 0
+//! ...
+//!  Wie geht es dir, mein Freund?
+//! ```
+//!
+//! ## Sentence embedding example
+//!
+//! ```bash
+//! cargo run --example t5 --release -- \
+//!   --model-id "t5-small" --prompt "A beautiful candle."
+//! ...
+//! [[[ 0.0515, -0.0541, -0.0761, ..., -0.0392,  0.1511, -0.0265],
+//!   [-0.0974,  0.0998, -0.1659, ..., -0.2450,  0.1738, -0.0164],
+//!   [ 0.0624, -0.1024,  0.0430, ..., -0.1388,  0.0564, -0.2962],
+//!   [-0.0389, -0.1173,  0.0026, ...,  0.1064, -0.1065,  0.0990],
+//!   [ 0.1300,  0.0027, -0.0326, ...,  0.0026, -0.0317,  0.0851]]]
+//! Tensor[[1, 5, 512], f32]
+//! Took 303.766583ms
+//! ```
 
 use crate::models::with_tracing::Embedding;
 use candle::{DType, Device, Module, Result, Tensor, D};