Popular GitHub Repositories for Parameter Adaptation

?? 「大模型」2小时完全从0训练26M的小参数GPT！? Train a 26M-parameter GPT from scratch in just 2h!

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

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

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Use PEFT or Full-parameter to finetune 450+ LLMs (Qwen2.5, InternLM3, GLM4, Llama3.3, Mistral, Yi1.5, Baichuan2, DeepSeek-R1, ...) and 150+ MLLMs (Qwen2.5-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2.5, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL2, Phi3.5-Vision, GOT-OCR2, ...).

A Flexible and Powerful Parameter Server for large-scale machine learning

StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

? 「大模型」1小时从0训练26M参数的视觉多模态VLM！? Train a 26M-parameter VLM from scratch in just 1 hours!

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台，我们欢迎开源爱好者发起任何有意义的pr！

POT : Python Optimal Transport

Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)

An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记