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Tensors and Dynamic neural networks in Python with strong GPU acceleration
💫 Toolkit to help you get started with Spec-Driven Development
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
A curated list of awesome Claude Skills, resources, and tools for customizing Claude AI workflows
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
A toolkit for developing and comparing reinforcement learning algorithms.
📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Fast and memory-efficient exact attention
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Train transformer language models with reinforcement learning.
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
Statistical learning methods, 统计学习方法(第2版)[李航] [笔记, 代码, notebook, 参考文献, Errata, lihang]
An Industrial Grade Federated Learning Framework
Denoising Diffusion Probabilistic Models
📚A curated list of Awesome LLM/VLM Inference Papers with Codes: Flash-Attention, Paged-Attention, WINT8/4, Parallelism, etc.🎉
Massively Parallel Deep Reinforcement Learning. 🔥
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs o…
This GitHub action helps to add cool dev metrics to your github profile Readme
AudioLDM: Generate speech, sound effects, music and beyond, with text.
MambaOut: Do We Really Need Mamba for Vision? (CVPR 2025)
An open-source framework for machine learning and other computations on decentralized data.
本项目分享了中山大学计算机学院本科和研究生阶段的课程资料、笔记、期末考试卷和其他实用的相关资源。希望对同学们的学习有所帮助❤️，如果喜欢记得给个star🌟
Master Federated Learning in 2 Hours—Run It on Your PC!
S-LoRA: Serving Thousands of Concurrent LoRA Adapters