repo stringlengths 6 65 | file_url stringlengths 81 311 | file_path stringlengths 6 227 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 15:31:58 2026-01-04 20:25:31 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/gemma3.rs | candle-transformers/src/models/gemma3.rs | //! Gemma LLM architecture (Google) inference implementation.
//!
//! See ["Introducing Gemma 3: The most capable model you can run on a single GPU or TPU"](https://blog.google/technology/developers/gemma-3/)
//!
//! Based on implementations from HuggingFace transformers.
use std::sync::Arc;
use candle::{DType, Devic... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/llama2_c_weights.rs | candle-transformers/src/models/llama2_c_weights.rs | //! Llama2 inference implementation.
//!
//! 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
use byteorder::{LittleEndian, ReadBytesExt};
use candle::{DType, Device, IndexOp, Result, Shape... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mamba.rs | candle-transformers/src/models/mamba.rs | //! Mamba inference implementation.
//!
//! See ["Mamba: Linear-Time Sequence Modeling with Selective State Spaces"](https://arxiv.org/abs/2312.00752)
//!
//! Based on reference implementation from the AlbertMamba project
//! A fast implementation of mamba for inference only.
//! Based on Laurent Mazare's rust implemen... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_blip_text.rs | candle-transformers/src/models/quantized_blip_text.rs | //! Quantized BLIP text module implementation.
//!
//! Provides the text decoder portion of the BLIP model with 8-bit quantization.
//! Uses a BERT-style transformer architecture for text processing.
//!
//! Key components:
//! - Text embeddings layer with position embeddings
//! - Multi-head self attention layers
//! ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/parler_tts.rs | candle-transformers/src/models/parler_tts.rs | //! Parler Model implementation for parler_tts text-to-speech synthesis
//!
//! Implements a transformer-based decoder architecture for generating audio tokens
//! from text using discrete tokens. The model converts text into audio segments
//! using multiple codebooks of quantized audio tokens.
//!
//! The model archi... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/convnext.rs | candle-transformers/src/models/convnext.rs | //! ConvNeXt implementation.
//!
//! 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:
//! - ๐ป [ConvNeXt](https://github.com/facebookresearch/... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/hiera.rs | candle-transformers/src/models/hiera.rs | //! Hiera inference implementation based on timm.
//!
//!
//! - ๐ป [Hiera](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/hiera.py)
//! - ๐ [Paper](https://arxiv.org/abs/2306.00989). Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
use candle::{Result, D};
use candle_... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/deepseek2.rs | candle-transformers/src/models/deepseek2.rs | #![allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
use std::{f32::consts::PI, sync::Arc};
use candle::{
shape::Dim, CpuStorage, CustomOp1, DType, Device, Error, IndexOp, Layout, Result, Shape,
Tensor, WithDType, D,
};
use candle_nn::{embedding, rms_norm, Activation, Embedding, Linear, Mo... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_llama2_c.rs | candle-transformers/src/models/quantized_llama2_c.rs | //! Quantized Llama2 model implementation.
//!
//! This provides an 8-bit quantized implementation of Meta's LLaMA2 language model
//! for reduced memory usage and faster inference.
//!
//! Key characteristics:
//! - Decoder-only transformer architecture
//! - RoPE position embeddings
//! - Grouped Query Attention
//! ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/blip_text.rs | candle-transformers/src/models/blip_text.rs | //! Implementation of BLIP text encoder/decoder.
//!
//! - ๐ [Paper](https://arxiv.org/abs/2201.12086). BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation"
//!
//! - โก [Interactive Wasm Example](https://huggingface.co/spaces/radames/Candle-BLIP-Image-Captioning)
//... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/phi.rs | candle-transformers/src/models/phi.rs | //! Microsoft Phi model implementation
//!
//! 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
//!
//! - โก [Interactive Wasm Example](https:/... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mixformer.rs | candle-transformers/src/models/mixformer.rs | //! MixFormer (Microsoft's Phi Architecture)
//!
//! See "Textbooks Are All You Need II: phi-1.5 technical report", Lin et al. 2023
//! - [Arxiv](https://arxiv.org/abs/2309.05463)
//! - [GitHub](https://huggingface.co/microsoft/phi-1_5)
//!
use crate::models::with_tracing::{linear, Embedding as E, Linear};
/// MixForm... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/siglip.rs | candle-transformers/src/models/siglip.rs | //! Siglip model implementation.
//!
//! Siglip architecture combining vision and language for zero-shot tasks.
//!
//! References:
//! - ๐ค [Model Card](https://huggingface.co/google/siglip-base-patch16-224)
//!
use crate::models::clip::div_l2_norm;
use candle::{IndexOp, Module, Result, Tensor, D};
use candle_nn::{la... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/granite.rs | candle-transformers/src/models/granite.rs | //! Granite is a Long Context Transformer Language Model.
//!
//! A high performance transformer model optimized for efficient processing
//! of very long context sequences
use super::with_tracing::{linear_no_bias as linear, Linear, RmsNorm};
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{emb... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_phi.rs | candle-transformers/src/models/quantized_phi.rs | //! Phi2 model implementation with quantization support.
//!
//! Phi2 is a 2.7B parameter language model using scaled-up Transformer decoder architecture.
//! This implementation provides quantization for reduced memory and compute usage.
//!
//! Key characteristics:
//! - Partial attention with learned mixing to reduc... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/gemma.rs | candle-transformers/src/models/gemma.rs | //! Gemma inference implementation.
//!
//! See ["Gemma: Open Models Based on Gemini Technology"](https://blog.google/technology/developers/gemma-open-ai-model/)
//!
//! Based on implementation from Google and PyTorch
use std::sync::Arc;
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{linear_b... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/glm4.rs | candle-transformers/src/models/glm4.rs | //! GLM-4 inference implementation.
//!
//! An open bilingual language model with 130B parameters.
//!
//! Based on implementation from [ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B)
use crate::models::with_tracing::{linear_b as linear, Linear};
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use c... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/efficientnet.rs | candle-transformers/src/models/efficientnet.rs | //! Implementation of EfficientBert, an efficient variant of BERT for computer vision tasks.
//!
//! See:
//! - ["EfficientBERT: Progressively Searching Multilayer Perceptron Architectures for BERT"](https://arxiv.org/abs/2201.00462)
//!
use candle::{Context, Result, Tensor, D};
use candle_nn as nn;
use nn::{Module, Va... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/qwen2.rs | candle-transformers/src/models/qwen2.rs | //! Qwen2 model implementation with quantization support.
//!
//! Qwen2 is a large language model from Alibaba optimized for efficiency.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Streaming decode support
//! - Grouped query attention (GQA)
//! - RMS... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_metavoice.rs | candle-transformers/src/models/quantized_metavoice.rs | //! Quantized MetaVoice model implementation.
//!
//! MetaVoice is a conditional text-to-speech model based on a transformer architecture.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Transformer-based autoregressive decoder
//! - Speaker conditioning
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_moondream.rs | candle-transformers/src/models/quantized_moondream.rs | //! Implementation of a quantized Moondream vision language model.
//!
//! Moondream is a lightweight vision-language model for image understanding and generation.
//! This module provides a quantized version for reduced memory usage and faster inference.
//!
//! Key features:
//! - ViT-based vision encoder
//! - Phi-2... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/llama2_c.rs | candle-transformers/src/models/llama2_c.rs | //! Llama2 inference implementation.
//!
//! See ["LLaMA 2: Open Foundation and Fine-Tuned Chat Models"](https://arxiv.org/abs/2307.09288)
//!
//! - โก [Interactive Wasm Example](https://huggingface.co/spaces/lmz/candle-llama2)
//! - ๐ป llama2.c [GH Link](https://github.com/karpathy/llama2.c)
//!
use candle::{DType, De... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/olmo2.rs | candle-transformers/src/models/olmo2.rs | //! OLMo 2 (Open Language Model) implementation
//!
//! See OLMo 2 model details at:
//! - [Hugging Face Collection](https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc)
//! - [OLMo 2 Paper](https://arxiv.org/abs/2501.00656)
//!
//!
use candle::{DType, Device, Module, Result, Tensor, D};
use cand... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/moondream.rs | candle-transformers/src/models/moondream.rs | //! 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)
//!
/... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/based.rs | candle-transformers/src/models/based.rs | //! Based from the Stanford Hazy Research group.
//!
//! See "Simple linear attention language models balance the recall-throughput tradeoff", Arora et al. 2024
//! - Simple linear attention language models balance the recall-throughput tradeoff. [Arxiv](https://arxiv.org/abs/2402.18668)
//! - [GitHub Rep](https://gith... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mobileone.rs | candle-transformers/src/models/mobileone.rs | //! # MobileOne
//!
//! MobileOne inference implementation based on timm and candle-repvgg
//!
//! See ["MobileOne: An Improved One millisecond Mobile Backbone"](https://arxiv.org/abs/2206.04040)
use candle::{DType, Result, Tensor, D};
use candle_nn::{
batch_norm, conv2d, conv2d_no_bias, linear, ops::sigmoid, Batc... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/t5.rs | candle-transformers/src/models/t5.rs | //! T5 model implementation.
//!
//! T5 (Text-to-Text Transfer Transformer) is a unified text-to-text transformer model.
//! This implementation follows the original model architecture.
//!
//! Key characteristics:
//! - Text-to-text framework
//! - Relative positional embeddings
//! - T5-specific layer normalization
/... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/encodec.rs | candle-transformers/src/models/encodec.rs | //! EnCodec neural audio codec based on the Encodec implementation.
//!
//! See ["High Fidelity Neural Audio Compression"](https://arxiv.org/abs/2210.13438)
//!
//! Based on implementation from [huggingface/transformers](https://github.com/huggingface/transformers/blob/main/src/transformers/models/encodec/modeling_enco... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/starcoder2.rs | candle-transformers/src/models/starcoder2.rs | //! StarCoder model implementation with quantization support.
//!
//! StarCoder is a large language model optimized for code generation.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Causal self-attention mechanism
//! - Multi-query attention (MQA)
//! ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/falcon.rs | candle-transformers/src/models/falcon.rs | //! Falcon language model inference implementation
//!
//! See ["Falcon: a new approach to large language models"](https://huggingface.co/blog/falcon)
//!
//! Based on implementation from [Huggingface Transformers](https://github.com/huggingface/transformers/blob/main/src/transformers/models/falcon)
use candle::{DType... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/bigcode.rs | candle-transformers/src/models/bigcode.rs | //! BigCode implementation in Rust based on the GPT-BigCode model.
//!
//! [StarCoder/BigCode](https://huggingface.co/bigcode/starcoderbase-1b) is a LLM
//! model specialized to code generation. The initial model was trained on 80
//! programming languages. See "StarCoder: A State-of-the-Art LLM for Code", Mukherjee et... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/olmo.rs | candle-transformers/src/models/olmo.rs | //! OLMo (Open Language Model) implementation
//!
//! See OLMo model details at:
//! - [Hugging Face](https://huggingface.co/allenai/OLMo)
//! - [OLMo Paper](https://allenai.org/olmo)
//!
//! The model uses:
//! - RoPE embeddings
//! - Sliding window attention
//! - Transformer architecture
//!
//! References:
//! - [H... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_rwkv_v5.rs | candle-transformers/src/models/quantized_rwkv_v5.rs | //! RWKV v5 model implementation with quantization support.
//!
//! RWKV v5 is an attention-free language model optimized for efficiency.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Linear attention mechanism
//! - GroupNorm layer normalization
//! - ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_rwkv_v6.rs | candle-transformers/src/models/quantized_rwkv_v6.rs | //! RWKV v6 model implementation with quantization support.
//!
//! RWKV is a linear attention model that combines the efficiency of RNNs
//! with the parallelizable training of Transformers. Version 6 builds on previous
//! versions with further optimizations.
//!
//! Key characteristics:
//! - Linear attention mechan... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_recurrent_gemma.rs | candle-transformers/src/models/quantized_recurrent_gemma.rs | //! Recurrent Gemma model implementation with quantization support.
//!
//! Gemma is a large language model optimized for efficiency.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Recurrent blocks with gated recurrent units
//! - Convolution and attenti... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_blip.rs | candle-transformers/src/models/quantized_blip.rs | //! BLIP model implementation with quantization support.
//!
//! BLIP is a vision-language model for image understanding and generation tasks.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Vision encoder using ViT architecture
//! - Text decoder using B... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mistral.rs | candle-transformers/src/models/mistral.rs | //! Mixtral Model, based on the Mistral architecture
//!
//! See Mistral and Mixtral at:
//! - [Hugging Face](https://huggingface.co/docs/transformers/model_doc/mixtral)
//! - [GitHub](https://github.com/mistralai/mistral-src)
//!
use crate::models::with_tracing::{linear_no_bias, Linear, RmsNorm};
/// Mistral LLM, htt... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_phi3.rs | candle-transformers/src/models/quantized_phi3.rs | //! Phi3 model implementation with quantization support.
//!
//! Phi3 is a language model intended for research purposes.
//! This implementation provides quantization for reduced memory usage.
//!
//! Key characteristics:
//! - Multi-head attention
//! - RMSNorm for layer normalization
//! - Rotary positional embeddin... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/persimmon.rs | candle-transformers/src/models/persimmon.rs | //! Persimmon Model
//!
//! 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... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/rwkv_v6.rs | candle-transformers/src/models/rwkv_v6.rs | //! RWKV v6 model implementation.
//!
//! 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.... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/quantized_mixformer.rs | candle-transformers/src/models/quantized_mixformer.rs | //! Module containing quantized MixFormer model implementation.
//!
//! MixFormer is an efficient transformer variant for text generation that uses
//! mixture-of-experts and parallel attention/feed-forward blocks.
//! This implementation provides quantization for reduced memory usage.
//!
//! Key features:
//! - Paral... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/stable_lm.rs | candle-transformers/src/models/stable_lm.rs | //! StableLM model implementation.
//!
//! StableLM is a family of language models trained by Stability AI.
//! This implementation supports the StableLM architecture.
//!
//! Key characteristics:
//! - Grouped query attention (GQA)
//! - Layer normalization
//! - Rotary positional embeddings (RoPE)
//! - Support for d... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/gemma2.rs | candle-transformers/src/models/gemma2.rs | //! Gemma LLM architecture (Google) inference implementation.
//!
//! See ["Gemma: Open Models Based on Gemini Technology"](https://blog.google/technology/developers/gemma-open-models/)
//!
//! Based on implementations from Google and OpenLLM
use std::sync::Arc;
use candle::{DType, Device, Module, Result, Tensor, D};... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/vgg.rs | candle-transformers/src/models/vgg.rs | //! VGG-16 model implementation.
//!
//! VGG-16 is a convolutional neural network architecture. It consists of 13
//! convolutional layers followed by 3 fully connected layers.
//!
//! Key characteristics:
//! - Conv layers with 3x3 filters
//! - Max pooling after every 2-3 conv layers
//! - Three fully connected layer... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/phi3.rs | candle-transformers/src/models/phi3.rs | //! Microsoft Phi-3 model implementation
//!
//! See Phi model details at:
//! - [Phi-3 Model](https://huggingface.co/microsoft/phi-3)
//!
//! The Phi series are decoder-only transformers designed for code and language tasks.
//! Key characteristics:
//! - Decoder-only transformer architecture
//! - RoPE embeddings
//!... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/qwen3_vl/config.rs | candle-transformers/src/models/qwen3_vl/config.rs | use candle_nn::Activation;
use crate::serde_default_fn;
serde_default_fn!(Activation, default_vision_hidden_act, Activation::Gelu);
serde_default_fn!(usize, default_in_channels, 3);
serde_default_fn!(usize, default_depth, 32);
serde_default_fn!(usize, default_hidden_size, 3584);
serde_default_fn!(usize, default_out_h... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/qwen3_vl/vision.rs | candle-transformers/src/models/qwen3_vl/vision.rs | use std::f64;
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{
embedding, layer_norm, linear, Activation, Embedding, LayerNorm, LayerNormConfig, Linear,
Module, VarBuilder,
};
use crate::models::qwen3_vl::conv3d_temporal_2::{Conv3dConfig, Conv3dNoBias};
use super::config::VisionConfi... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/qwen3_vl/conv3d_temporal_2.rs | candle-transformers/src/models/qwen3_vl/conv3d_temporal_2.rs | //! Conv3dConfig assuming a temporal patch size of 2
use candle::{IndexOp, Module, Result, Tensor};
use candle_nn::{Conv2d, Conv2dConfig, VarBuilder};
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct Conv3dConfig {
pub padding: usize,
pub stride: usize,
pub dilation: usize,
pub groups: usize,
}... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/qwen3_vl/text.rs | candle-transformers/src/models/qwen3_vl/text.rs | use std::sync::{Arc, Mutex};
use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{
embedding, kv_cache::KvCache, linear, linear_b, rms_norm, Activation, Embedding, Linear,
Module, RmsNorm, VarBuilder,
};
use super::config::TextConfig;
#[derive(Debug, Clone)]
pub struct RotaryEmbedding {
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/qwen3_vl/mod.rs | candle-transformers/src/models/qwen3_vl/mod.rs | #![allow(clippy::cast_possible_truncation, clippy::cast_precision_loss)]
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::VarBuilder;
use text::Qwen3VLTextModel;
use vision::Qwen3VLVisionModel;
pub mod config;
mod conv3d_temporal_2;
mod text;
mod vision;
pub use config::Config;
use crate::mod... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/z_image/text_encoder.rs | candle-transformers/src/models/z_image/text_encoder.rs | //! Z-Image Text Encoder (Qwen3 Adapter)
//!
//! This module provides a Qwen3-based text encoder for Z-Image.
//! Key difference from the standard Qwen3 model:
//! - Returns the **second-to-last layer** hidden states (hidden_states[-2])
//! - Does NOT apply the final RMSNorm
use crate::models::with_tracing::{linear_b,... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/z_image/sampling.rs | candle-transformers/src/models/z_image/sampling.rs | //! Sampling utilities for Z-Image model.
use candle::{DType, Device, Result, Tensor};
/// Generate initial Gaussian noise
///
/// # Arguments
/// * `batch_size` - Batch size
/// * `channels` - Number of channels (typically 16, VAE latent channels)
/// * `height` - Height (latent space, i.e., image_height / 16)
/// *... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/z_image/vae.rs | candle-transformers/src/models/z_image/vae.rs | //! Z-Image VAE (AutoEncoderKL) - Diffusers Format
//!
//! This VAE implementation uses the diffusers weight naming format,
//! which is different from the Flux autoencoder original format.
//!
//! Key differences from Flux autoencoder:
//! 1. Weight paths: `encoder.down_blocks.{i}.resnets.{j}.*` vs `encoder.down.{i}.b... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/z_image/mod.rs | candle-transformers/src/models/z_image/mod.rs | /*
* @Author: SpenserCai
* @Date: 2026-01-02 11:35:48
* @version:
* @LastEditors: SpenserCai
* @LastEditTime: 2026-01-02 11:48:26
* @Description: file content
*/
//! Z-Image Model
//!
//! Z-Image is a text-to-image generation model from Alibaba using Flow Matching.
//!
//! - ๐ค [Hugging Face Model](https://huggi... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/z_image/preprocess.rs | candle-transformers/src/models/z_image/preprocess.rs | //! Input preprocessing utilities for Z-Image
//!
//! Provides padding and mask construction to convert variable-length inputs
//! into fixed-shape batch tensors.
use candle::{DType, Device, Result, Tensor};
use super::transformer::SEQ_MULTI_OF;
/// Preprocessed inputs structure
#[derive(Debug, Clone)]
pub struct Pr... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/z_image/transformer.rs | candle-transformers/src/models/z_image/transformer.rs | //! Z-Image Transformer (ZImageTransformer2DModel)
//!
//! Core transformer implementation for Z-Image text-to-image generation.
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::{linear, linear_no_bias, VarBuilder};
use crate::models::with_tracing::RmsNorm;
// ==================== Flas... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/z_image/scheduler.rs | candle-transformers/src/models/z_image/scheduler.rs | //! FlowMatch Euler Discrete Scheduler for Z-Image
//!
//! Implements the flow matching scheduler used in Z-Image generation.
use candle::{Result, Tensor};
/// FlowMatchEulerDiscreteScheduler configuration
#[derive(Debug, Clone, serde::Deserialize)]
pub struct SchedulerConfig {
#[serde(default = "default_num_trai... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/openclip/text_model.rs | candle-transformers/src/models/openclip/text_model.rs | //! Text encoder as used in most OpenCLIP pretrained models
//! https://github.com/mlfoundations/open_clip
use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn::{
embedding, layer_norm, linear, ops::softmax_last_dim, Embedding, LayerNorm, Linear, Module,
VarBuilder,
};
#[derive(Debug, Clone)]
pub st... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/openclip/mod.rs | candle-transformers/src/models/openclip/mod.rs | //! Open Contrastive Language-Image Pre-Training
//!
//! Open Contrastive Language-Image Pre-Training (OpenCLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - ๐ป [GH Link](https://github.com/mlfoundations/open_clip)
//! - ๐ [Paper](https://arxiv.org/abs/2212.07143)
//!
//! ## Overview... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/wuerstchen/prior.rs | candle-transformers/src/models/wuerstchen/prior.rs | use super::common::{AttnBlock, ResBlock, TimestepBlock};
use candle::{DType, Result, Tensor, D};
use candle_nn::VarBuilder;
#[derive(Debug)]
struct Block {
res_block: ResBlock,
ts_block: TimestepBlock,
attn_block: AttnBlock,
}
#[derive(Debug)]
pub struct WPrior {
projection: candle_nn::Conv2d,
con... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/wuerstchen/attention_processor.rs | candle-transformers/src/models/wuerstchen/attention_processor.rs | use candle::{Module, Result, Tensor};
use candle_nn::{linear, Linear, VarBuilder};
// A simplified version of:
// https://github.com/huggingface/diffusers/blob/119ad2c3dc8a8fb8446a83f4bf6f20929487b47f/src/diffusers/models/attention_processor.py#L38
#[derive(Debug)]
pub struct Attention {
to_q: Linear,
to_k: Li... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/wuerstchen/diffnext.rs | candle-transformers/src/models/wuerstchen/diffnext.rs | use super::common::{AttnBlock, GlobalResponseNorm, LayerNormNoWeights, TimestepBlock, WLayerNorm};
use candle::{DType, Module, Result, Tensor, D};
use candle_nn::VarBuilder;
#[derive(Debug)]
pub struct ResBlockStageB {
depthwise: candle_nn::Conv2d,
norm: WLayerNorm,
channelwise_lin1: candle_nn::Linear,
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/wuerstchen/mod.rs | candle-transformers/src/models/wuerstchen/mod.rs | //! Wรผrstchen Efficient Diffusion Model
//!
//! Wรผrstchen is an efficient diffusion model architecture for generating images using
//! a two-stage approach with a small decoder and prior network.
//!
//! - ๐ป [GH Link](https://github.com/dome272/Wuerstchen)
//! - ๐ค [HF Link](https://github.com/huggingface/diffusers/bl... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/wuerstchen/common.rs | candle-transformers/src/models/wuerstchen/common.rs | use candle::{DType, Module, Result, Tensor, D};
use candle_nn::VarBuilder;
// https://github.com/huggingface/diffusers/blob/19edca82f1ff194c07317369a92b470dbae97f34/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py#L22
#[derive(Debug)]
pub struct WLayerNorm {
eps: f64,
}
impl WLayerNorm {
pub f... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/wuerstchen/paella_vq.rs | candle-transformers/src/models/wuerstchen/paella_vq.rs | use super::common::LayerNormNoWeights;
use candle::{Module, Result, Tensor};
use candle_nn::VarBuilder;
#[derive(Debug)]
pub struct MixingResidualBlock {
norm1: LayerNormNoWeights,
depthwise_conv: candle_nn::Conv2d,
norm2: LayerNormNoWeights,
channelwise_lin1: candle_nn::Linear,
channelwise_lin2: c... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/wuerstchen/ddpm.rs | candle-transformers/src/models/wuerstchen/ddpm.rs | use candle::{Result, Tensor};
#[derive(Debug, Clone)]
pub struct DDPMWSchedulerConfig {
scaler: f64,
s: f64,
}
impl Default for DDPMWSchedulerConfig {
fn default() -> Self {
Self {
scaler: 1f64,
s: 0.008f64,
}
}
}
pub struct DDPMWScheduler {
init_alpha_cump... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/pixtral/llava.rs | candle-transformers/src/models/pixtral/llava.rs | use candle::{Module, Result, Tensor};
use candle_nn::{linear, Linear, VarBuilder};
use super::vision_model;
use crate::models::mistral;
#[derive(serde::Deserialize, Debug, Clone)]
pub struct Config {
pub projector_hidden_act: candle_nn::Activation,
pub text_config: mistral::Config,
pub vision_config: visi... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/pixtral/vision_model.rs | candle-transformers/src/models/pixtral/vision_model.rs | use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{linear_b, rms_norm, Linear, RmsNorm, VarBuilder};
fn default_act() -> candle_nn::Activation {
candle_nn::Activation::Silu
}
fn default_hidden_size() -> usize {
1024
}
fn default_intermediate_size() -> usize {
4096
}
fn default_num_c... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/pixtral/mod.rs | candle-transformers/src/models/pixtral/mod.rs | //! Pixtral Language-Image Pre-Training
//!
//! Pixtral is an architecture trained for multimodal learning
//! using images paired with text descriptions.
//!
//! - ๐ป Transformers Python [reference implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/pixtral)
//! - ๐ [Blog Pos... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/clip/text_model.rs | candle-transformers/src/models/clip/text_model.rs | //! Contrastive Language-Image Pre-Training
//!
//! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - [GH](https://github.com/openai/CLIP)
//! - [Code](https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/s... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/clip/vision_model.rs | candle-transformers/src/models/clip/vision_model.rs | //! Contrastive Language-Image Pre-Training
//!
//! 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/m... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/clip/mod.rs | candle-transformers/src/models/clip/mod.rs | //! Contrastive Language-Image Pre-Training
//!
//! Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - ๐ป [GH Link](https://github.com/openai/CLIP)
//! - ๐ป Transformers Python [reference implementation](https://github.com/huggingface/transform... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/segment_anything/mask_decoder.rs | candle-transformers/src/models/segment_anything/mask_decoder.rs | use candle::{IndexOp, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use super::transformer::TwoWayTransformer;
#[derive(Debug)]
struct MlpMaskDecoder {
layers: Vec<super::Linear>,
sigmoid_output: bool,
span: tracing::Span,
}
impl MlpMaskDecoder {
fn new(
input_dim: usize,
hidd... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/segment_anything/tiny_vit.rs | candle-transformers/src/models/segment_anything/tiny_vit.rs | // Adapted from:
// https://github.com/ChaoningZhang/MobileSAM/blob/master/mobile_sam/modeling/tiny_vit_sam.py
use candle::{IndexOp, Result, Tensor, D};
use candle_nn::{Conv2dConfig, Module, VarBuilder};
const MBCONV_EXPAND_RATIO: usize = 4;
const MLP_RATIO: usize = 4;
const LOCAL_CONV_SIZE: usize = 3;
const IMG_SIZE:... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/segment_anything/mod.rs | candle-transformers/src/models/segment_anything/mod.rs | //! 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. //! This model provides a robust and fast image segmentation pipeline that can be tweaked via
//! some prompting (requesting some points to be i... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/segment_anything/sam.rs | candle-transformers/src/models/segment_anything/sam.rs | use candle::{DType, IndexOp, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use super::image_encoder::ImageEncoderViT;
use super::mask_decoder::MaskDecoder;
use super::prompt_encoder::PromptEncoder;
use super::tiny_vit::{tiny_vit_5m, TinyViT};
const PROMPT_EMBED_DIM: usize = 256;
pub const IMAGE_SIZE: usize = ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/segment_anything/image_encoder.rs | candle-transformers/src/models/segment_anything/image_encoder.rs | use candle::{DType, IndexOp, Result, Tensor};
use candle_nn::{layer_norm, LayerNorm, Module, VarBuilder};
#[derive(Debug)]
struct PatchEmbed {
proj: candle_nn::Conv2d,
span: tracing::Span,
}
impl PatchEmbed {
fn new(
in_chans: usize,
embed_dim: usize,
k_size: usize,
stride:... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/segment_anything/prompt_encoder.rs | candle-transformers/src/models/segment_anything/prompt_encoder.rs | use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn::VarBuilder;
#[derive(Debug)]
struct PositionEmbeddingRandom {
positional_encoding_gaussian_matrix: Tensor,
}
impl PositionEmbeddingRandom {
fn new(num_pos_feats: usize, vb: VarBuilder) -> Result<Self> {
let positional_encoding_gaussian_ma... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/segment_anything/transformer.rs | candle-transformers/src/models/segment_anything/transformer.rs | use candle::{Result, Tensor};
use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
#[derive(Debug)]
struct Attention {
q_proj: Linear,
k_proj: Linear,
v_proj: Linear,
out_proj: Linear,
num_heads: usize,
}
impl Attention {
fn new(
embedding_dim: usize,
num_heads: ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/chinese_clip/text_model.rs | candle-transformers/src/models/chinese_clip/text_model.rs | //! Chinese contrastive Language-Image Pre-Training
//!
//! Chinese contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - ๐ป [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)
//! - ๐ป [HF](https://github.com/huggingface/transformers/blob/5af... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/chinese_clip/vision_model.rs | candle-transformers/src/models/chinese_clip/vision_model.rs | //! Chinese contrastive Language-Image Pre-Training
//!
//! Chinese contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - ๐ป [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)
//! - ๐ป [GH](https://github.com/huggingface/transformers/blob/5af... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/chinese_clip/mod.rs | candle-transformers/src/models/chinese_clip/mod.rs | //! Chinese contrastive Language-Image Pre-Training
//!
//! Chinese contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
//! pairs of images with related texts.
//!
//! - ๐ป [GH Link](https://github.com/OFA-Sys/Chinese-CLIP)
//! - ๐ป Transformers Python [reference implementation](https://github.... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/flux/sampling.rs | candle-transformers/src/models/flux/sampling.rs | use candle::{Device, Result, Tensor};
pub fn get_noise(
num_samples: usize,
height: usize,
width: usize,
device: &Device,
) -> Result<Tensor> {
let height = height.div_ceil(16) * 2;
let width = width.div_ceil(16) * 2;
Tensor::randn(0f32, 1., (num_samples, 16, height, width), device)
}
#[de... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/flux/autoencoder.rs | candle-transformers/src/models/flux/autoencoder.rs | use candle::{Result, Tensor, D};
use candle_nn::{conv2d, group_norm, Conv2d, GroupNorm, VarBuilder};
// https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/modules/autoencoder.py#L9
#[derive(Debug, Clone)]
pub struct Config {
pub resolution: usize,
pub in_channels: ... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/flux/quantized_model.rs | candle-transformers/src/models/flux/quantized_model.rs | use super::model::{attention, timestep_embedding, Config, EmbedNd};
use crate::quantized_nn::{linear, linear_b, Linear};
use crate::quantized_var_builder::VarBuilder;
use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn::{LayerNorm, RmsNorm};
fn layer_norm(dim: usize, vb: VarBuilder) -> Result<LayerNorm> {
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/flux/model.rs | candle-transformers/src/models/flux/model.rs | use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn::{LayerNorm, Linear, RmsNorm, VarBuilder};
// https://github.com/black-forest-labs/flux/blob/727e3a71faf37390f318cf9434f0939653302b60/src/flux/model.py#L12
#[derive(Debug, Clone)]
pub struct Config {
pub in_channels: usize,
pub vec_in_dim: usize,
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/flux/mod.rs | candle-transformers/src/models/flux/mod.rs | //! Flux Model
//!
//! Flux is a 12B rectified flow transformer capable of generating images from text descriptions.
//!
//! - ๐ค [Hugging Face Model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
//! - ๐ป [GitHub Repository](https://github.com/black-forest-labs/flux)
//! - ๐ [Blog Post](https://blackfores... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/voxtral/voxtral_llama.rs | candle-transformers/src/models/voxtral/voxtral_llama.rs | use crate::models::with_tracing::{linear_no_bias as linear, Linear, RmsNorm};
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{embedding, Embedding, Module, VarBuilder};
use serde::Deserialize;
use std::collections::HashMap;
pub const DEFAULT_MAX_SEQ_LEN: usize = 4096;
#[derive(Debug, Clone, P... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/voxtral/model.rs | candle-transformers/src/models/voxtral/model.rs | use super::voxtral_llama::{VoxtralLlama, VoxtralLlamaCache, VoxtralLlamaConfig};
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::{
layer_norm, linear, linear_no_bias, Conv1d, Dropout, LayerNorm, Linear, VarBuilder,
};
use rand::Rng;
#[derive(Debug, Clone)]
pub struct VoxtralEncoderC... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | true |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/voxtral/mod.rs | candle-transformers/src/models/voxtral/mod.rs | pub mod audio;
pub mod model;
pub mod voxtral_llama;
pub use audio::extract_features;
pub use model::{
VoxtralCache, VoxtralConfig, VoxtralEncoder, VoxtralEncoderConfig,
VoxtralForConditionalGeneration, VoxtralGenerationConfig, VoxtralMultiModalProjector,
};
pub use voxtral_llama::{VoxtralLlama, VoxtralLlamaCa... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/voxtral/audio.rs | candle-transformers/src/models/voxtral/audio.rs | use candle::{DType, Device, Error, Tensor};
use crate::models::whisper::audio::{log_mel_spectrogram_, Float};
pub fn pcm_to_mel<T: Float>(samples: &[T], filters: &[T]) -> Vec<T> {
log_mel_spectrogram_(
samples,
filters,
super::N_FFT,
super::HOP_LENGTH,
super::N_MELS,
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/llava/config.rs | candle-transformers/src/models/llava/config.rs | use std::collections::HashMap;
use crate::models::{
clip::{text_model::Activation, vision_model::ClipVisionConfig},
llama::{Config, LlamaEosToks},
};
use serde::{Deserialize, Serialize};
// original config from liuhaotian/llava
#[derive(Serialize, Deserialize, Debug, Clone)]
pub struct LLaVAConfig {
pub a... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/llava/utils.rs | candle-transformers/src/models/llava/utils.rs | pub fn get_anyres_image_grid_shape(
image_size: (u32, u32),
grid_pinpoints: &[(u32, u32)],
patch_size: u32,
) -> (u32, u32) {
let (width, height) = select_best_resolution(image_size, grid_pinpoints);
(width / patch_size, height / patch_size)
}
pub fn select_best_resolution(
original_size: (u32,... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/llava/mod.rs | candle-transformers/src/models/llava/mod.rs | //! 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. The architecture implements the training-free projection technique.
//!
//! - ๐ป[GH Link](https://github.com/haotian... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mmdit/projections.rs | candle-transformers/src/models/mmdit/projections.rs | use candle::{Module, Result, Tensor};
use candle_nn as nn;
pub struct Qkv {
pub q: Tensor,
pub k: Tensor,
pub v: Tensor,
}
pub struct Mlp {
fc1: nn::Linear,
act: nn::Activation,
fc2: nn::Linear,
}
impl Mlp {
pub fn new(
in_features: usize,
hidden_features: usize,
v... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mmdit/embedding.rs | candle-transformers/src/models/mmdit/embedding.rs | use candle::{bail, DType, Module, Result, Tensor};
use candle_nn as nn;
pub struct PatchEmbedder {
proj: nn::Conv2d,
}
impl PatchEmbedder {
pub fn new(
patch_size: usize,
in_channels: usize,
embed_dim: usize,
vb: nn::VarBuilder,
) -> Result<Self> {
let proj = nn::co... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mmdit/model.rs | candle-transformers/src/models/mmdit/model.rs | // Implement the MMDiT model originally introduced for Stable Diffusion 3 (https://arxiv.org/abs/2403.03206),
// as well as the MMDiT-X variant introduced for Stable Diffusion 3.5-medium (https://huggingface.co/stabilityai/stable-diffusion-3.5-medium)
// This follows the implementation of the MMDiT model in the ComfyUI... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mmdit/mod.rs | candle-transformers/src/models/mmdit/mod.rs | //! Mix of Multi-scale Dilated and Traditional Convolutions
//!
//! 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.
//!
//! - ๐ [Research Paper](https://arxiv.org/abs/2403.03206)
//! - ๐ป Co... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/mmdit/blocks.rs | candle-transformers/src/models/mmdit/blocks.rs | use candle::{Module, Result, Tensor, D};
use candle_nn as nn;
use super::projections::{AttnProjections, Mlp, Qkv, QkvOnlyAttnProjections};
pub struct ModulateIntermediates {
gate_msa: Tensor,
shift_mlp: Tensor,
scale_mlp: Tensor,
gate_mlp: Tensor,
}
pub struct DiTBlock {
norm1: LayerNormNoAffine,... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
huggingface/candle | https://github.com/huggingface/candle/blob/a4ad7c79666958c38b9afc0e0c3e3499ab8991d8/candle-transformers/src/models/whisper/quantized_model.rs | candle-transformers/src/models/whisper/quantized_model.rs | use super::Config;
use crate::quantized_nn::{layer_norm, linear, linear_no_bias, Embedding, Linear};
pub use crate::quantized_var_builder::VarBuilder;
use candle::{Device, IndexOp, Result, Tensor, D};
use candle_nn::{Conv1d, Conv1dConfig, LayerNorm, Module};
fn conv1d(
in_channels: usize,
out_channels: usize,
... | rust | Apache-2.0 | a4ad7c79666958c38b9afc0e0c3e3499ab8991d8 | 2026-01-04T15:42:50.663313Z | false |
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