OpenICL is an open-source framework to facilitate research, development, and prototyping of in-context learning.

Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

Pytorch implementation of various Knowledge Distillation (KD) methods.

A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content…

Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.

Running local Language Language Models (LLM) to perform Retrieval-Augmented Generation (RAG)

A curated list of awesome Machine Learning frameworks, libraries and software.

Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.

Awesome list for LLM pruning.

深度学习系统笔记，包含深度学习数学基础知识、神经网络基础部件详解、深度学习炼丹策略、模型压缩算法详解、和大模型基础和推理性能优化分析。

《开源大模型食用指南》基于Linux环境快速部署开源大模型，更适合中国宝宝的部署教程

AI for Mathematics (AI4Math) paper list

An implementation of online data mixing for the Pile dataset, based on the GPT-NeoX library.

A curated list of resources for Learning with Noisy Labels

顶不住的运筹优化

A curated list of papers and resources about the distribution shift in machine learning.

A curated list of awesome papers about calibration

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc

ImageNet Testbed, associated with the paper "Measuring Robustness to Natural Distribution Shifts in Image Classification."

Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

The Official Repository for "Generalized OOD Detection: A Survey"

💎 The Principles of OOD (SOLID) based on Uncle Bob articles.

Out-of-distribution detection, robustness, and generalization resources. The repository contains a professionally curated list of papers, tutorials, books, videos, articles and open-source librarie…

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

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

A collection of AWESOME things about domian adaptation