kashif

pytorch-transformer-ts

Repository of Transformer based PyTorch Time Series Models

diffusers

vq-tr

VQ-TR repository

gluon-ts

GluonTS - Probabilistic Time Series Modeling in Python

time_match

iTransformer

Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting".

trl

Train transformer language models with reinforcement learning.

ForecastPFN

accelerate

🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

alignment-handbook

Robust recipes for to align language models with human and AI preferences

blog

Public repo for HF blog posts

datasets

🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

llmtime

vector-quantize-pytorch

Vector Quantization, in Pytorch

chronos-forecasting

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

distrax

evaluate

A library for easily evaluating machine learning models and datasets.

HoMM

High order Moment Models

hopfield-layers

Hopfield Networks is All You Need

lit-gpt

Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

mamba

minRF

mlx-examples

Examples in the MLX framework

notebooks

Notebooks using the Hugging Face libraries 🤗

pytorch-lightning

Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.

RWKV-LM

RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.

summarize_from_feedback_details

timesfm

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

xformers

Hackable and optimized Transformers building blocks, supporting a composable construction.