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RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RN…
The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)
Graph Neural Network Library for PyTorch
本项目旨在收集开源的表格智能任务数据集（比如表格问答、表格-文本生成等），将原始数据整理为指令微调格式的数据并微调LLM，进而增强LLM对于表格数据的理解，最终构建出专门面向表格智能任务的大型语言模型。
We collect papers about "large language models (LLM) for table-related tasks", e.g., using LLM for Table QA task. “表格+LLM”相关论文整理
xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
A standard framework for modelling Deep Learning Models for tabular data
DeepTables: Deep-learning Toolkit for Tabular data
Implementations of Papers that I read, you can read my breakdown in my blog
Tabular Deep Learning Library for PyTorch
StableLM: Stability AI Language Models
Inference code for "TabDPT: Scaling Tabular Foundation Models on Real Data"
Training code for TabDPT: Scaling Tabular Foundation Models on Real Data
Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)
Repository for CARTE: Context-Aware Representation of Table Entries
A library built upon PyTorch for building embeddings on discrete event sequences using self-supervision
Hackable and optimized Transformers building blocks, supporting a composable construction.
A concise but complete full-attention transformer with a set of promising experimental features from various papers
An easy/swift-to-adapt PyTorch-Lighting template. 套壳模板，简单易用，稍改原来Pytorch代码，即可适配Lightning。You can translate your previous Pytorch code much easier using this template, and keep your freedom to edit a…
A resource for learning about Machine learning & Deep Learning
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Repository for TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
Scaling In-context Learning from Few-shot to 1,024-shot on Tabular ML
Experiments on public datasets for pytorch-lifestream library
EBES: EASY BENCHMARKING FOR EVENT SEQUENCES