This repository collects papers for "A Survey on Knowledge Distillation of Large Language Models". We break down KD into Knowledge Elicitation and Distillation Algorithms, and explore the Skill & Vertical Distillation of LLMs.

A pytorch implementation of paper 'Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation', https://arxiv.org/abs/1905.08094

Deep Hash Distillation for Image Retrieval - ECCV 2022

(Unofficial) Data-Distortion Guided Self-Distillation for Deep Neural Networks (AAAI 2019)

[ACL 2024] The official codebase for the paper "Self-Distillation Bridges Distribution Gap in Language Model Fine-tuning".

A minimalist unofficial implementation of "Self-Distillation from the Last Mini-Batch for Consistency Regularization"

A generalized self-supervised training paradigm for unimodal and multimodal alignment and fusion.

Self-Distillation with weighted ground-truth targets; ResNet and Kernel Ridge Regression

Bayesian Optimization Meets Self-Distillation, ICCV 2023

Pytorch implementation of "Emerging Properties in Self-Supervised Vision Transformers" (a.k.a. DINO)

Official implementation of Self-Distillation for Gaussian Processes

Self-Distillation and Knowledge Distillation Experiments with PyTorch.

Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation

A simple and efficient implementation of Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (I-JEPA)

Enhancing LLMs with LoRA

Self supervised learning through self distillation with no labels (DINO) with Vision Transformers on the PCAM dataset.