aider is AI pair programming in your terminal

Supporting code for: Video Enriched Retrieval Augmented Generation Using Aligned Video Captions

Firefly: 大模型训练工具，支持训练Qwen2、Yi1.5、Phi-3、Llama3、Gemma、MiniCPM、Yi、Deepseek、Orion、Xverse、Mixtral-8x7B、Zephyr、Mistral、Baichuan2、Llma2、Llama、Qwen、Baichuan、ChatGLM2、InternLM、Ziya2、Vicuna、Bloom等大模型

QLoRA: Efficient Finetuning of Quantized LLMs

Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation

official implementation of Neural Weight Search for Task Incremental Learning.

A playbook for systematically maximizing the performance of deep learning models.

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

Port of the Read the Docs theme to Jekyll to use with GitHub Pages.

✨A performant, accessible, progressive React portfolio template that uses the GitHub REST API.

Pytorch Implemtation of Meta-Learning with Latent Embedding Optimization

Quickest way to share everything about your research within a single app

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Code for the paper "Representational Continuity for Unsupervised Continual Learning" (ICLR 22)

Collections of model quantization algorithms. Any issues, please contact Peng Chen ([email protected])

Yet another PyTorch Trainer and some core components for deep learning.

[CVPR 2021] KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

PyTorch implementation of VQ-VAE + WaveNet by [Chorowski et al., 2019] and VQ-VAE on speech signals by [van den Oord et al., 2017]

AR1* with Latent Replay, implemented in PyTorch

74.3% MobileNetV3-Large and 67.2% MobileNetV3-Small model on ImageNet

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet…

Tutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101

Three steps to train your own model for action recognition based on CNN and LSTM by PyTorch.

Must-read papers on graph neural networks (GNN)

DeepWalk - Deep Learning for Graphs

Code for the paper: "edGNN: A simple and powerful GNN for labeled graphs"