AdapterHub

@AdapterHub

A central repository for pre-trained adapter modules in transformers! Active maintainers: @clifapt @h_sterz @LeonEnglaender @timo_imhof @PfeiffJo

AdapterHub.ml

Joined May 2020

Pinned
AdapterHub
@AdapterHub
Nov 24, 2023
🎉 Exciting news! The new Adapters library for modular and parameter-efficient transfer learning is out! 🤖 Now simplified & disentangled from @huggingface pip install adapters pip install transformers 📄arxiv.org/abs/2311.11077 👾 github.com/adapter-hub/ad… #EMNLP2023 🧵👇
AdapterHub
@AdapterHub
May 21, 2025
🚀Adapters v1.2 is out!🚀 We've made Adapters incredibly flexible: Add adapter support to ANY Transformer architecture with minimal code! We used this to add 8 new models out-of-the-box, incl. ModernBERT, Gemma3 & Qwen3! Explore this +2 new adapter methods in this thread👇(1/5)
AdapterHub
@AdapterHub
Jan 29, 2025
🎁 A new update of the Adapters library is out! Check out all the novelties, changes & fixes here:
Adapters v1.1.0 · adapter-hub adapters · Discussion #788
From github.com
AdapterHub
@AdapterHub
Aug 12, 2024
🎉Adapters 1.0 is here!🚀 Our open-source library for modular and parameter-efficient fine-tuning got a major upgrade! v1.0 is packed with new features (ReFT, Adapter Merging, QLoRA, ...), new models & improvements! Blog: adapterhub.ml/blog/2024/08/a… Highlights in the thread! 🧵👇
AdapterHub
@AdapterHub
Jul 2, 2024
📢 New preprint 🎉 We - the AdapterHub team - present the M2QA benchmark to evaluate joint domain and language transfer! 🔬 Key highlight: We show that adapter-based methods on small language models can reach the performance of Llama 3 on M2QA! 🚀 👇
Leon Engländer
@LeonEnglaender
Jul 2, 2024
📢 New preprint 🎉 We introduce "M2QA: Multi-domain Multilingual Question Answering", a benchmark for evaluating joint language and domain transfer. We present 5 key findings - one of them: Current transfer methods are insufficient, even for LLMs! 📜arxiv.org/abs/2407.01091 🧵👇