torch-adapters VS VL_adapter

Compare torch-adapters vs VL_adapter and see what are their differences.

torch-adapters

Small Library of PyTorch Adaptation modules (by ma2za)

VL_adapter

PyTorch code for "VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks" (CVPR2022) (by ylsung)
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torch-adapters VL_adapter
1 1
9 193
- -
6.1 0.0
10 months ago over 1 year ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

torch-adapters

Posts with mentions or reviews of torch-adapters. We have used some of these posts to build our list of alternatives and similar projects.

VL_adapter

Posts with mentions or reviews of VL_adapter. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-07.

What are some alternatives?

When comparing torch-adapters and VL_adapter you can also consider the following projects:

LongLoRA - Code and documents of LongLoRA and LongAlpaca (ICLR 2024 Oral)

LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

MMD-GAN - Training MMD GANs.

xTuring - Build, customize and control you own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHXuSJEk6

VLDet - [ICLR 2023] PyTorch implementation of VLDet (https://arxiv.org/abs/2211.14843)

LLM-Adapters - Code for our EMNLP 2023 Paper: "LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models"