GGG-c

FuseAI

FuseAI Project

peft-llm-code

Replication package of the paper "Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models".

LaMDA

FuseAI

FuseAI Project

PiSSA

PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

MEFT

zett

Code for Zero-Shot Tokenizer Transfer

RecyclableTuning

pipeline_peft_for_llms

This repository is about our work, which appear in EMNLP 2023 main.

SN-Net

[CVPR 2023 Highlight] This is the official implementation of "Stitchable Neural Networks".

Stitched_LLaMA

[CVPR 2024] A framework to fine-tune LLaMAs on instruction-following task and get many Stitched LLaMAs with customized number of parameters, e.g., Stitched LLaMA 8B, 9B, and 10B...

VPGTrans

Codes for VPGTrans: Transfer Visual Prompt Generator across LLMs. VL-LLaMA, VL-Vicuna.

llama3

The official Meta Llama 3 GitHub site

ResidualPrompts

Residual Prompt Tuning: a method for faster and better prompt tuning.

PEM_composition

[NeurIPS 2023] Github repository for "Composing Parameter-Efficient Modules with Arithmetic Operations"

ScaLearn

ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale

PyContinual

PyContinual (An Easy and Extendible Framework for Continual Learning)

pyvene

Stanford NLP Python Library for Understanding and Improving PyTorch Models via Interventions

MEND

hypter

Zero-shot Learning by Generating Task-specific Adapters

loretta

[NAACL 24 Oral] LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models

peft

PET_Scaling

Exploring the Impact of Model Scaling on Parameter-efficient Tuning Methods

DecT

Source code for ACL 2023 paper Decoder Tuning: Efficient Language Understanding as Decoding

Black-Box-Tuning

ICML'2022: Black-Box Tuning for Language-Model-as-a-Service & EMNLP'2022: BBTv2: Towards a Gradient-Free Future with Large Language Models

lq-lora

t-few

Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

kms

KMS 激活服务，slmgr 命令激活 Windows 系统、Office

MiMi

🔥 🔥 [WACV2024] Mini but Mighty: Finetuning ViTs with Mini Adapters

DiffPruning

Parameter Efficient Transfer Learning with Diff Pruning