adapter

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

A powerful HTTP client for Dart and Flutter, which supports global settings, Interceptors, FormData, aborting and canceling a request, files uploading and downloading, requests timeout, custom adapters, etc.

An assembled flutter application framework.

The bullet proof, fast and easy to use adapter library, which minimizes developing time to a fraction...

Fast and versatile Adapter for RecyclerView which regroups several features into one library to considerably improve the user experience :-)

Build, personalize and control your 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

Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts

Python toolkit for Chinese LLMs, with flexible batch capacity, structured real-time visulization and automated accumulation for streaming, and explicit feedback on vendor-native parameter validation.

Train an adapter for any embedding model in under a minute

diffusion lora chinese tutorial，虚拟idol训练中文教程

A tiny, vendor-neutral LLM client library — one interface (LLMClient) with pluggable adapters for OpenAI, AWS Bedrock and Google Gemini, or bring your own backend.

Awesome List of Vision Language Prompt Papers

Codes and datasets for our ICASSP2023 paper, Evaluating parameter-efficient transfer learning approaches on SURE benchmark for speech understanding

Multi-domain Recommendation with Adapter Tuning

Train linear embedding adapters with triplet loss to align retrieval embeddings with your queries (RAG).

🎈 Structure-aware adapter fine-tuning PLMs, with high training speed and impressive performance (Journal of Chemical Information and Modeling 2024).

[CVPR 2025 Highlight] Meta LoRA / MetaPEFT: Meta-Learning Hyperparameters for Parameter-Efficient Fine-Tuning (LoRA, Adapter, Prompt Tuning) — Automatic hyperparameter optimization via bi-level meta-learning

A Lightweight, Configuration-Driven, Flexible Fine-Tuning Framework for 🤗 Diffusers

[ACMMM'24] MoBA: Mixture of Bi-directional Adapter for Multi-modal Sarcasm Detection