jvpoulos

ollama

Get up and running with Llama 3, Mistral, Gemma 2, and other large language models.

grok-1

Grok open release

llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

flash-attention

Fast and memory-efficient exact attention

bertviz

BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

bitsandbytes

Accessible large language models via k-bit quantization for PyTorch.

pytorch_tabular

A standard framework for modelling Deep Learning Models for tabular data

rtdl

Research on Tabular Deep Learning: Papers & Packages

tab-transformer-pytorch

Implementation of TabTransformer, attention network for tabular data, in Pytorch

Awesome-LLM-Safety

A curated list of safety-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights into the safety implications, challenges, and advancements surrounding these powerful models.

saint

The official PyTorch implementation of recent paper - SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

iTransformer

Unofficial implementation of iTransformer - SOTA Time Series Forecasting using Attention networks, out of Tsinghua / Ant group

UniTS

A unified multi-task time series model.

TimeSeries

Implementation of deep learning models for time series in PyTorch.

TabSurvey

Experiments on Tabular Data Models

be_great

A novel approach for synthesizing tabular data using pretrained large language models

electra-pytorch

A simple and working implementation of Electra, the fastest way to pretrain language models from scratch, in Pytorch

GenePT

multivariate-attention-tcn

ViTST

[NeurIPS 2023] The official repo for the paper: "Time Series as Images: Vision Transformer for Irregularly Sampled Time Series"."

pygformula

The pygformula implements the parametric g-formula in Python. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean of an outcome under hypothetical treatment strategies specified by the user.

refined-dpo

Refined Direct Preference Optimization with Synthetic Data for Behavioral Alignment of LLMs

parseRPDR

Parse and Manipulate Research Patient Data Registry ('RPDR') Text Queries

MIMICIV_FMs_public

Sample end-to-end pipeline over MIMIC-IV demonstrating the Event Stream GPT code base.

TESAR-CDE

Code for the paper "Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time" (ICML 2023)

rpdrtools

Library of utility functions for handling RPDR (Research Patient Data Registry) data.

SurvCI

SurvCI & SurvCI-Info:Counterfactual Inference using Balanced Representations for Parametric Deep Survival Analysis

pyelixhauser

Python package to detect clinical morbodity in ICD10 and ICD9 codes

drotr

Doubly-robust methods for creating optimal treatment rules