Module Docs (#2624)

* update whisper

* update llama2c

* update t5

* update phi and t5

* add a blip model

* qlamma doc

* add two new docs

* add docs and emoji

* additional models

* openclip

* pixtral

* edits on the  model docs

* update yu

* update a fe wmore models

* add persimmon

* add model-level doc

* names

* update module doc

* links in heira

* remove empty URL

* update more hyperlinks

* updated hyperlinks

* more links

* Update mod.rs

---------

Co-authored-by: Laurent Mazare <laurent.mazare@gmail.com>

candle-transformers/src/models/blip.rs

@@ -1,8 +1,11 @@
 //! Based on the BLIP paper from Salesforce Research.
 //!
-//! See "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation"
-//! - [Arxiv](https://arxiv.org/abs/2201.12086)
-//! - [Github](https://github.com/salesforce/BLIP)
+//! The blip-image-captioning model can generate captions for an input image.
+//!
+//! - ⚡ [Interactive Wasm Example](https://huggingface.co/spaces/radames/Candle-BLIP-Image-Captioning)
+//! - 💻 [GH Link](https://github.com/salesforce/BLIP)
+//! - 🤗 [HF Link](https://huggingface.co/Salesforce/blip-image-captioning-base)
+//! - 📝 [Paper](https://arxiv.org/abs/2201.12086)
 //!
 
 use super::blip_text;

candle-transformers/src/models/blip_text.rs

@@ -1,9 +1,12 @@
 //! Implementation of BLIP text encoder/decoder.
 //!
-//! See "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation"
-//! https://arxiv.org/abs/2201.12086
+//! - 📝 [Paper](https://arxiv.org/abs/2201.12086). BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation"
+//!
+//! - ⚡ [Interactive Wasm Example](https://huggingface.co/spaces/radames/Candle-BLIP-Image-Captioning)
+//! - 💻 [GH Link](https://github.com/salesforce/BLIP)
+//! - 🤗 [HF Link](https://huggingface.co/Salesforce/blip-image-captioning-base)
+//! - 📝 [Paper](https://arxiv.org/abs/2201.12086)
 //!
-
 use super::with_tracing::{linear, Embedding, Linear};
 use candle::{Module, Result, Tensor, D};
 use candle_nn::{layer_norm, LayerNorm, VarBuilder};

candle-transformers/src/models/chatglm.rs

@@ -1,10 +1,8 @@
 //! Implementation of the ChatGLM2/3 models from THUDM.
 //!
-//! See:
-//! - ChatGLM3: ["ChatGLM3: Advancing Multilingual Conversational Language Models with High-Quality Data"](https://github.com/THUDM/ChatGLM3)
-//! - ChatGLM2: ["ChatGLM2: An Open Bilingual Chat LLM"](https://github.com/THUDM/ChatGLM2-6B)
+//! - 💻 [Github](https://github.com/THUDM/ChatGLM3) ChatGLM3: Advancing Multilingual Conversational Language Models with High-Quality Data
+//! - 💻 [Github](https://github.com/THUDM/ChatGLM2-6B) ChatGLM2-6B.
 //!
-
 use crate::models::with_tracing::{linear_b as linear, Linear};
 use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
 use candle_nn::VarBuilder;

candle-transformers/src/models/chinese_clip/mod.rs

@@ -3,10 +3,9 @@
 //! Chinese contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
 //! pairs of images with related texts.
 //!
-//! - [GH Link](https://github.com/OFA-Sys/Chinese-CLIP)
-//! - Transformers Python [reference implementation](https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/chinese_clip/modeling_chinese_clip.py)
+//! - 💻 [GH Link](https://github.com/OFA-Sys/Chinese-CLIP)
+//! - 💻 Transformers Python [reference implementation](https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/chinese_clip/modeling_chinese_clip.py)
 //!
-
 use candle::{Module, Result, Tensor, D};
 use candle_nn as nn;
 

candle-transformers/src/models/chinese_clip/text_model.rs

@@ -3,8 +3,8 @@
 //! Chinese contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
 //! pairs of images with related texts.
 //!
-//! https://github.com/OFA-Sys/Chinese-CLIP
-//! https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/chinese_clip/modeling_chinese_clip.py
+//! - 💻 [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)
+//! - 💻 [HF](https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/chinese_clip/modeling_chinese_clip.py)
 
 use candle::{DType, Device, IndexOp, Module, Result, Tensor};
 use candle_nn as nn;
@@ -67,7 +67,7 @@ impl Default for ChineseClipTextConfig {
 }
 
 impl ChineseClipTextConfig {
-    /// referer: https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16/blob/main/config.json
+    /// [referer](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16/blob/main/config.json)
     pub fn clip_vit_base_patch16() -> Self {
         Self {
             vocab_size: 21128,

candle-transformers/src/models/chinese_clip/vision_model.rs

@@ -3,8 +3,8 @@
 //! Chinese contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
 //! pairs of images with related texts.
 //!
-//! https://github.com/OFA-Sys/Chinese-CLIP
-//! https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/chinese_clip/modeling_chinese_clip.py
+//! - 💻 [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)
+//! - 💻 [GH](https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/chinese_clip/modeling_chinese_clip.py_
 
 use candle::{DType, IndexOp, Module, Result, Shape, Tensor, D};
 use candle_nn as nn;
@@ -49,7 +49,7 @@ impl Default for ChineseClipVisionConfig {
 }
 
 impl ChineseClipVisionConfig {
-    /// referer: https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16/blob/main/config.json
+    /// [referer](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16/blob/main/config.json)
     pub fn clip_vit_base_patch16() -> Self {
         Self {
             hidden_size: 768,

candle-transformers/src/models/clip/mod.rs

@@ -3,8 +3,10 @@
 //! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
 //! pairs of images with related texts.
 //!
-//! - [GH Link](https://github.com/openai/CLIP)
-//! - Transformers Python [reference implementation](https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip)
+//! - 💻 [GH Link](https://github.com/openai/CLIP)
+//! - 💻 Transformers Python [reference implementation](https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip)
+//! - 🤗 [HF Model](https://huggingface.co/openai/clip-vit-large-patch14-336)
+//!
 
 use self::{
     text_model::{Activation, ClipTextTransformer},

candle-transformers/src/models/clip/text_model.rs

@@ -3,8 +3,8 @@
 //! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
 //! pairs of images with related texts.
 //!
-//! https://github.com/openai/CLIP
-//! https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip
+//! - [GH](https://github.com/openai/CLIP)
+//! - [Code](https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip)
 
 use candle::{DType, Device, IndexOp, Result, Tensor, D};
 use candle_nn as nn;

candle-transformers/src/models/codegeex4_9b.rs

@@ -1,8 +1,9 @@
 //! CodeGeeX4 - A multi-language code generation model
 //!
-//! See "CodeGeeX: A Pre-Trained Model For Code Generation with Multilingual Evaluations on HumanEval-X", Qian et al. 2023
-//! - [Arxiv](https://arxiv.org/abs/2303.17568)
-//! - [Github](https://github.com/THUDM/CodeGeeX)
+//! A Pre-Trained Model For Code Generation with Multilingual Evaluations on HumanEval-X"
+//!
+//! - 📝 [Arxiv](https://arxiv.org/abs/2303.17568)
+//! - 💻 [Github](https://github.com/THUDM/CodeGeeX)
 //!
 
 use crate::models::with_tracing::{linear_b as linear, Linear};

candle-transformers/src/models/convmixer.rs

@@ -1,10 +1,10 @@
 //! ConvMixer implementation.
 //!
 //! See "Patches Are All You Need?" by Trockman et al. 2022
-//! - [Arxiv](https://arxiv.org/abs/2201.09792)
-//! - [Github](https://github.com/locuslab/convmixer)
 //!
-
+//! - 📝 [Arxiv](https://arxiv.org/abs/2201.09792)
+//! - 💻 [Github](https://github.com/locuslab/convmixer)
+//!
 use candle::Result;
 use candle_nn::{batch_norm, Conv2dConfig, Module, VarBuilder};
 

candle-transformers/src/models/convnext.rs

@@ -1,13 +1,16 @@
 //! ConvNeXt implementation.
 //!
-//! See ["A ConvNet for the 2020s" Liu et al. 2022](https://arxiv.org/abs/2201.03545)
-//! and
-//! ["ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders" Woo et al. 2023](https://arxiv.org/abs/2301.00808)
+//! This candle implementation uses a pre-trained ConvNeXt network for inference. The
+//! classification head has been trained on the ImageNet dataset and returns the
+//! probabilities for the top-5 classes.
 //!
 //! Original code:
-//!  - [ConvNeXt](https://github.com/facebookresearch/ConvNeXt/)
-//!  - [ConvNeXt-V2](https://github.com/facebookresearch/ConvNeXt-V2/)
-//!  - [timm](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/convnext.py)
+//! - 💻 [ConvNeXt](https://github.com/facebookresearch/ConvNeXt/)
+//! - 💻 [ConvNeXt-V2](https://github.com/facebookresearch/ConvNeXt-V2/)
+//! - 💻 [timm](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/convnext.py)
+//! - 📝 [Paper](https://arxiv.org/abs/2201.03545) A ConvNet for the 2020s
+//! - 📝 [Paper](https://arxiv.org/abs/2301.00808) ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
+//!
 
 use candle::shape::ShapeWithOneHole;
 use candle::{Result, D};

candle-transformers/src/models/flux/mod.rs

@@ -2,9 +2,9 @@
 //!
 //! Flux is a 12B rectified flow transformer capable of generating images from text descriptions.
 //!
-//! - [Hugging Face Model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
-//! - [GitHub Repository](https://github.com/black-forest-labs/flux)
-//! - [Blog Post](https://blackforestlabs.ai/announcing-black-forest-labs/)
+//! - 🤗 [Hugging Face Model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
+//! - 💻 [GitHub Repository](https://github.com/black-forest-labs/flux)
+//! - 📝 [Blog Post](https://blackforestlabs.ai/announcing-black-forest-labs/)
 //!
 //! # Usage
 //!

candle-transformers/src/models/hiera.rs

@@ -1,9 +1,8 @@
-//! [Hiera] inference implementation based on timm.
+//! Hiera inference implementation based on timm.
 //!
-//! See "[Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles]"
-//! [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles]: https://arxiv.org/abs/2306.00989
 //!
-//! [Hiera]: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/hiera.py
+//! - 💻 [Hiera](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/hiera.py)
+//! - 📝 [Paper](https://arxiv.org/abs/2306.00989). Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
 
 use candle::{Result, D};
 use candle_nn::{conv2d, layer_norm, linear, ops::softmax, Conv2dConfig, Func, VarBuilder};

candle-transformers/src/models/llama2_c.rs

@@ -2,7 +2,9 @@
 //!
 //! See ["LLaMA 2: Open Foundation and Fine-Tuned Chat Models"](https://arxiv.org/abs/2307.09288)
 //!
-//! Based on the [llama2.c](https://github.com/karpathy/llama2.c) implementation
+//! - ⚡ [Interactive Wasm Example](https://huggingface.co/spaces/lmz/candle-llama2)
+//! - 💻 llama2.c [GH Link](https://github.com/karpathy/llama2.c)
+//!
 
 use candle::{DType, Device, IndexOp, Result, Tensor, D};
 use candle_nn::linear_no_bias as linear;

candle-transformers/src/models/llava/mod.rs

@@ -1,13 +1,12 @@
 //! The LLaVA (Large Language and Vision Assistant) model.
 //!
 //! This provides the main model implementation combining a vision tower (CLIP) with
-//! language model (Llama) for multimodal capabilities.
+//! language model (Llama) for multimodal capabilities. The architecture implements the training-free projection technique.
 //!
-//! The architecture implements the training-free projection technique from the paper:
-//! [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485).
-//!
-//! - [GH Link](https://github.com/haotian-liu/LLaVA/tree/main)
+//! - 💻[GH Link](https://github.com/haotian-liu/LLaVA/tree/main)
+//! - 📝 [Paper](https://arxiv.org/abs/2304.08485)/ Visual Instruction Tuning
 //!
+
 pub mod config;
 pub mod utils;
 

candle-transformers/src/models/mimi/mod.rs

@@ -1,9 +1,27 @@
 //! mimi model
 //!
-//! Mimi is a state-of-the-art audio neural codec.
+//! [Mimi](https://huggingface.co/kyutai/mimi) is a state of the art audio
+//! compression model using an encoder/decoder architecture with residual vector
+//! quantization. The candle implementation supports streaming meaning that it's
+//! possible to encode or decode a stream of audio tokens on the flight to provide
+//! low latency interaction with an audio model.
 //!
-//! - [HuggingFace Model Card](https://huggingface.co/kyutai/mimi)
-//! - [GitHub](https://github.com/kyutai-labs/moshi)
+//! - 🤗 [HuggingFace Model Card](https://huggingface.co/kyutai/mimi)
+//! - 💻 [GitHub](https://github.com/kyutai-labs/moshi)
+//!
+//!
+//! # Example
+//! ```bash
+//! # Generating some audio tokens from an audio files.
+//! wget https://github.com/metavoiceio/metavoice-src/raw/main/assets/bria.mp3
+//! cargo run --example mimi \
+//!   --features mimi --release -- \
+//!   audio-to-code bria.mp3 bria.safetensors
+//!
+//! # And decoding the audio tokens back into a sound file.
+//! cargo run --example mimi
+//!   --features mimi --release -- \
+//!   code-to-audio bria.safetensors bria.wav
 //!
 
 // Copyright (c) Kyutai, all rights reserved.

candle-transformers/src/models/mmdit/mod.rs

@@ -3,9 +3,15 @@
 //! Mix of Multi-scale Dilated and Traditional Convolutions (MMDiT) is an architecture
 //! introduced for Stable Diffusion 3, with the MMDiT-X variant used in Stable Diffusion 3.5.
 //!
-//! - [Research Paper](https://arxiv.org/abs/2403.03206)
-//! - ComfyUI [reference implementation](https://github.com/comfyanonymous/ComfyUI/blob/78e133d0415784924cd2674e2ee48f3eeca8a2aa/comfy/ldm/modules/diffusionmodules/mmdit.py)
-//! - Stability-AI [MMDiT-X implementation](https://github.com/Stability-AI/sd3.5/blob/4e484e05308d83fb77ae6f680028e6c313f9da54/mmditx.py)
+//! - 📝 [Research Paper](https://arxiv.org/abs/2403.03206)
+//! - 💻 ComfyUI [reference implementation](https://github.com/comfyanonymous/ComfyUI/blob/78e133d0415784924cd2674e2ee48f3eeca8a2aa/comfy/ldm/modules/diffusionmodules/mmdit.py)
+//! - 💻 Stability-AI [MMDiT-X implementation](https://github.com/Stability-AI/sd3.5/blob/4e484e05308d83fb77ae6f680028e6c313f9da54/mmditx.py)
+
+//! - ⚡ [Interactive Wasm Example](https://huggingface.co/spaces/radames/Candle-BLIP-Image-Captioning)
+//! - 💻 [GH Link](https://github.com/salesforce/BLIP)
+//! - 🤗 [HF Link](https://huggingface.co/Salesforce/blip-image-captioning-base)
+//! - 📝 [Paper](https://arxiv.org/abs/2201.12086)
+//!
 
 pub mod blocks;
 pub mod embedding;

candle-transformers/src/models/mod.rs

@@ -1,3 +1,19 @@
+//! Candle implementations for various deep learning models
+//!
+//! This crate provides implementations of popular machine learning models and architectures for different modalities.
+//!
+//!  - Large language models: [`llama`], [`phi3`], [`mamba`], [`mixtral`], [`bert`], ...
+//!  - Text to text models: [`t5`], ...
+//!  - Image to text models: [`blip`], ...
+//!  - Text to image models: [`stable_diffusion`] and [`wuerstchen`], ...
+//!  - Audio models: [`whisper`], [`encodec`], [`metavoice`], [`parler_tts`], ...
+//!  - Computer vision models: [`dinov2`], [`convmixer`], [`efficientnet`], ...
+//!  
+//! Some of the models also have quantized variants, e.g.  [`quantized_blip`], [`quantized_llama`] and  [`quantized_qwen2`].
+//!
+//! The implementations aim to be readable while maintaining good performance. For more information
+//! on each model see the model's module docs in the links below.
+
 pub mod based;
 pub mod beit;
 pub mod bert;

candle-transformers/src/models/openclip/mod.rs

@@ -3,7 +3,11 @@
 //! Open Contrastive Language-Image Pre-Training (OpenCLIP) is an architecture trained on
 //! pairs of images with related texts.
 //!
-//! - [GH Link](https://github.com/mlfoundations/open_clip)
+//! - 💻 [GH Link](https://github.com/mlfoundations/open_clip)
+//! - 📝 [Paper](https://arxiv.org/abs/2212.07143)
 //!
+//! ## Overview
+//!
+//! ![](https://raw.githubusercontent.com/mlfoundations/open_clip/main/docs/CLIP.png)
 
 pub mod text_model;

candle-transformers/src/models/persimmon.rs

@@ -1,17 +1,15 @@
 //! Persimmon Model
 //!
-//! A transformer language model for efficient inference and general-purpose tasks. See Persimmon model details at:
-//! - [Hugging Face](https://huggingface.co/adept/persimmon-8b-base)
-//!
-//! The model uses a standard transformer architecture with:
+//! A transformer language model for efficient inference and general-purpose tasks. The model uses a standard transformer architecture with:
 //! - Layer normalization for Q/K attention
 //! - RoPE embeddings with partial rotary factor
 //! - ReLU activation
 //! - Separate number of attention heads and KV heads
 //!
 //! References:
-//! - [Hugging Face Implementation](https://github.com/huggingface/transformers/blob/main/src/transformers/models/persimmon/modeling_persimmon.py)
-//! - [Persimmon Config](https://github.com/huggingface/transformers/blob/main/src/transformers/models/persimmon/configuration_persimmon.py)
+//! - 💻 [Hugging Face Implementation](https://github.com/huggingface/transformers/blob/main/src/transformers/models/persimmon/modeling_persimmon.py)
+//! - 💻 [Persimmon Config](https://github.com/huggingface/transformers/blob/main/src/transformers/models/persimmon/configuration_persimmon.py)
+//! - 🤗 [Hugging Face](https://huggingface.co/adept/persimmon-8b-base)
 //!
 
 use candle::DType;

candle-transformers/src/models/phi.rs

@@ -1,18 +1,15 @@
 //! Microsoft Phi model implementation
 //!
-//! See Phi model details at:
-//! - [Phi-2 Model](https://huggingface.co/microsoft/phi-2)
-//!
 //! The Phi series are decoder-only transformers designed for code and language tasks.
+//!
 //! Key characteristics:
 //! - Decoder-only transformer architecture
 //! - RoPE embeddings
 //! - Layer normalization
 //! - QK normalization
 //!
-//! References:
-//! - [Hugging Face Implementation](https://huggingface.co/microsoft/phi-2)
-//! - [Alternative Implementation](https://huggingface.co/microsoft/phi-2/tree/main)
+//! - ⚡ [Interactive Wasm Example](https://huggingface.co/spaces/radames/Candle-phi1-phi2-wasm-demo)
+//! - 🤗 [HF Link](https://huggingface.co/microsoft/phi-2)
 //!
 
 use crate::models::with_tracing::{layer_norm, linear, Embedding, LayerNorm, Linear};

candle-transformers/src/models/pixtral/mod.rs

@@ -3,10 +3,10 @@
 //! Pixtral is an architecture trained for multimodal learning
 //! using images paired with text descriptions.
 //!
-//! - Transformers Python [reference implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/pixtral)
-//! - [Blog Post](https://mistral.ai/news/pixtral-12b/) -
-//! - [HF Model Card](https://huggingface.co/mistralai/Pixtral-12B-2409) -
-//! - [HF Community Model Card](https://huggingface.co/mistral-community/pixtral-12b).
+//! - 💻 Transformers Python [reference implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/pixtral)
+//! - 📝 [Blog Post](https://mistral.ai/news/pixtral-12b/)
+//! - 🤗 [HF Model Card](https://huggingface.co/mistralai/Pixtral-12B-2409)
+//! - 🤗 [HF Community Model Card](https://huggingface.co/mistral-community/pixtral-12b)
 //!
 //! # Example
 //!

candle-transformers/src/models/quantized_llama.rs

@@ -10,9 +10,10 @@
 //! - Optimized memory usage through quantization
 //! - Configurable model sizes and parameter counts
 //!
-//! References:
-//! - [LLaMA Paper](https://arxiv.org/abs/2302.13971)
-//! - [LLaMA Model](https://github.com/facebookresearch/llama)
+//! - 💻 [GH Link](https://github.com/facebookresearch/llama)
+//! - 📝 [Paper](https://arxiv.org/abs/2302.13971)
+//!
+//! ![](https://raw.githubusercontent.com/huggingface/candle/main/candle-examples/examples/quantized/assets/aoc.gif)
 //!
 
 use std::collections::HashMap;

candle-transformers/src/models/quantized_t5.rs

@@ -11,9 +11,9 @@
 //! - Support for 8-bit quantization
 //!
 //! References:
-//! - [T5 Paper](https://arxiv.org/abs/1910.10683)
-//! - [Model Card](https://huggingface.co/t5-base)
-//! - Original model from [T5](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py)
+//! - 📝 [T5 Paper](https://arxiv.org/abs/1910.10683)
+//! - 🤗 [Model Card](https://huggingface.co/t5-base)
+//! - 🤗 Original model from [T5](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py)
 
 use crate::models::t5::{deserialize_feed_forward_proj_activation, ActivationWithOptionalGating};
 use crate::models::with_tracing::QMatMul;

candle-transformers/src/models/qwen2.rs

@@ -11,8 +11,7 @@
 //! - Support for 8-bit quantization
 //!
 //! References:
-//! - [Qwen2 Model](https://huggingface.co/Qwen/Qwen2-7B)
-//! - [Model Card](https://huggingface.co/Qwen/Qwen2-7B)
+//! - 🤗 [Qwen2 Model](https://huggingface.co/Qwen/Qwen2-7B)
 //!
 
 use crate::models::with_tracing::{linear, linear_no_bias, Linear, RmsNorm};

candle-transformers/src/models/repvgg.rs

@@ -1,8 +1,5 @@
 //! RepVGG inference implementation
 //!
-//! See "RepVGG: Making VGG-style ConvNets Great Again" Ding et al. 2021
-//! https://arxiv.org/abs/2101.03697
-//!
 //! Key characteristics:
 //! - Efficient inference architecture through structural reparameterization
 //! - Single 3x3 conv layer after fusing 3x3 branch, 1x1 branch and identity branch
@@ -10,7 +7,7 @@
 //! - High accuracy with VGG-like plain architecture and training
 //!
 //! References:
-//! - [RepVGG Paper](https://arxiv.org/abs/2101.03697)
+//! - [RepVGG Paper](https://arxiv.org/abs/2101.03697). RepVGG: Making VGG-style ConvNets Great Again
 //! - [Official Implementation](https://github.com/DingXiaoH/RepVGG)
 //!
 

candle-transformers/src/models/siglip.rs

@@ -3,7 +3,7 @@
 //! Siglip architecture combining vision and language for zero-shot tasks.
 //!
 //! References:
-//! - [Model Card](https://huggingface.co/google/siglip-base-patch16-224)
+//! - 🤗 [Model Card](https://huggingface.co/google/siglip-base-patch16-224)
 //!
 
 use crate::models::clip::div_l2_norm;

candle-transformers/src/models/stable_diffusion/clip.rs

@@ -3,7 +3,7 @@
 //! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
 //! pairs of images with related texts.
 //!
-//! https://github.com/openai/CLIP
+//! - [CLIP](https://github.com/openai/CLIP)
 use candle::{DType, Device, Result, Tensor, D};
 use candle_nn as nn;
 use candle_nn::Module;

candle-transformers/src/models/stable_diffusion/ddpm.rs

@@ -104,7 +104,7 @@ impl DDPMScheduler {
         };
         let current_beta_t = 1. - alpha_prod_t / alpha_prod_t_prev;
 
-        // For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
+        // For t > 0, compute predicted variance βt (see formula (6) and (7) from [the pdf](https://arxiv.org/pdf/2006.11239.pdf))
         // and sample from it to get previous sample
         // x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         let variance = (1. - alpha_prod_t_prev) / (1. - alpha_prod_t) * current_beta_t;

candle-transformers/src/models/stable_diffusion/euler_ancestral_discrete.rs

@@ -1,12 +1,7 @@
 //! Ancestral sampling with Euler method steps.
 //!
-//! Reference implementation in Rust:
-//!
-//! https://github.com/pykeio/diffusers/blob/250b9ad1898af41e76a74c0d8d4292652823338a/src/schedulers/euler_ancestral_discrete.rs
-//!
-//! Based on the original [`k-diffusion` implementation by Katherine Crowson][kd].
+//! Based on the original [`k-diffusion` implementation by Katherine Crowson]( https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72).
 ///
-/// [kd]: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72
 use super::{
     schedulers::{
         betas_for_alpha_bar, BetaSchedule, PredictionType, Scheduler, SchedulerConfig,
@@ -29,7 +24,7 @@ pub struct EulerAncestralDiscreteSchedulerConfig {
     pub steps_offset: usize,
     /// prediction type of the scheduler function, one of `epsilon` (predicting
     /// the noise of the diffusion process), `sample` (directly predicting the noisy sample`)
-    /// or `v_prediction` (see section 2.4 https://imagen.research.google/video/paper.pdf)
+    /// or `v_prediction` (see [section 2.4](https://imagen.research.google/video/paper.pdf))
     pub prediction_type: PredictionType,
     /// number of diffusion steps used to train the model
     pub train_timesteps: usize,

candle-transformers/src/models/stable_diffusion/mod.rs

@@ -3,9 +3,9 @@
 //! Stable Diffusion is a latent text-to-image diffusion model capable of
 //! generating photo-realistic images given any text input.
 //!
-//! - [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.
+//! - 💻 [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

candle-transformers/src/models/stable_diffusion/resnet.rs

@@ -3,7 +3,8 @@
 //! Some Residual Network blocks used in UNet models.
 //!
 //! Denoising Diffusion Implicit Models, K. He and al, 2015.
-//! https://arxiv.org/abs/1512.03385
+//! - [Paper](https://arxiv.org/abs/1512.03385)
+//!
 use crate::models::with_tracing::{conv2d, Conv2d};
 use candle::{Result, Tensor, D};
 use candle_nn as nn;

candle-transformers/src/models/stable_diffusion/schedulers.rs

@@ -43,7 +43,7 @@ pub enum PredictionType {
 
 /// Time step spacing for the diffusion process.
 ///
-/// "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
+/// "linspace", "leading", "trailing" corresponds to annotation of Table 2. of the [paper](https://arxiv.org/abs/2305.08891)
 #[derive(Debug, Clone, Copy)]
 pub enum TimestepSpacing {
     Leading,

candle-transformers/src/models/stable_lm.rs

@@ -10,7 +10,7 @@
 //! - Support for different model sizes (3B, 7B)
 //!
 //! References:
-//! - [Model Card](https://huggingface.co/stabilityai/stablelm-3b-4e1t)
+//! - 🤗 [Model Card](https://huggingface.co/stabilityai/stablelm-3b-4e1t)
 //!
 
 use crate::models::with_tracing::{linear, linear_no_bias, Linear};

candle-transformers/src/models/starcoder2.rs

@@ -11,8 +11,8 @@
 //! - Support for 8-bit quantization
 //!
 //! References:
-//! - [StarCoder Paper](https://arxiv.org/abs/2305.06161)
-//! - [Model Card](https://huggingface.co/bigcode/starcoder)
+//! - 📝 [StarCoder Paper](https://arxiv.org/abs/2305.06161)
+//! - 🤗 [Model Card](https://huggingface.co/bigcode/starcoder)
 //!
 
 use candle::{DType, Device, Module, Result, Tensor, D};

candle-transformers/src/models/t5.rs

@@ -11,9 +11,10 @@
 //! - Support for sequence-to-sequence tasks
 //!
 //! References:
-//! - [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)
+//! - ⚡ [Interactive Wasm Example](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm)
+//! - 💻[GH Model](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py)
+//! - 🤗 [HF Link](https://huggingface.co/docs/transformers/model_doc/t5)
+//! - 📝 [T5 Paper](https://arxiv.org/abs/1910.10683)
 //!
 //! # Encoder-decoder example:
 //!

candle-transformers/src/models/whisper/mod.rs

@@ -1,10 +1,14 @@
 //! Whisper Model Implementation
 //!
 //! Whisper is an automatic speech recognition (ASR) system trained on large amounts
-//! of multilingual and multitask supervised data collected from the web.
+//! of multilingual and multitask supervised data collected from the web. It can be used to
+//! convert audio files (in the `.wav` format) to text. Supported features include
+//! language detection as well as multilingual speech recognition.
+//!
+//! - ⚡ [Interactive Wasm Example](https://huggingface.co/spaces/lmz/candle-whisper)
+//! - 💻 [GH Link](https://github.com/openai/whisper)
+//! - 💻 Transformers Python [reference implementation](https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py)
 //!
-//! - [GH Link](https://github.com/openai/whisper)
-//! - Transformers Python [reference implementation](https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py)
 //!
 pub mod audio;
 pub mod model;

candle-transformers/src/models/wuerstchen/mod.rs

@@ -3,10 +3,17 @@
 //! Würstchen is an efficient diffusion model architecture for generating images using
 //! a two-stage approach with a small decoder and prior network.
 //!
-//! - [Paper Link](https://openreview.net/pdf?id=gU58AyJlYz)
-//! - [GH Link](https://github.com/dome272/Wuerstchen)
-//! - [Reference Implementation](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py)
+//! - 💻 [GH Link](https://github.com/dome272/Wuerstchen)
+//! - 🤗 [HF Link](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py)
+//! - 📝 [Paper](https://openreview.net/pdf?id=gU58AyJlYz)
 //!
+//! ## Example
+//!
+//! <div align=center>
+//!   <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/wuerstchen/assets/cat.jpg" alt="" width=320>
+//!   <p>"Anthropomorphic cat dressed as a fire fighter"</p>
+//! </div>
+
 pub mod attention_processor;
 pub mod common;
 pub mod ddpm;

candle-transformers/src/models/yi.rs

@@ -1,7 +1,12 @@
 //! Yi model implementation.
 //!
-//! Yi is a decoder-only large language model trained by 01.AI.
-//! It follows a standard transformer architecture similar to Llama.
+//! This candle implementation uses a pre-trained Yi decoder-only large language model for inference.
+//! The model was trained by 01.AI and follows a standard transformer architecture similar to LLaMA.
+//!
+//! Original code:
+//! - 💻 [Yi Model](https://huggingface.co/01-ai/Yi-6B)
+//! - 💻 [Yi Modeling Code](https://huggingface.co/01-ai/Yi-6B/blob/main/modeling_yi.py)
+//! - 📝 [Technical Report](https://arxiv.org/abs/2403.04652) Yi: Open Foundation Models by 01.AI
 //!
 //! Key characteristics:
 //! - Multi-head attention with rotary positional embeddings
@@ -9,9 +14,6 @@
 //! - SwiGLU activation in feed-forward layers
 //! - Grouped-query attention for efficient inference
 //!
-//! References:
-//! - [Yi Model](https://huggingface.co/01-ai/Yi-6B)
-//! - [Hugging Face](https://huggingface.co/01-ai/Yi-6B/blob/main/modeling_yi.py)
 
 use crate::models::with_tracing::{linear_no_bias, Linear, RmsNorm};
 use candle::{DType, Device, Module, Result, Tensor, D};