Popular GitHub Repositories for Meta Layer

The Memory layer for AI Agents

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

This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches.

Automated Machine Learning with scikit-learn

Supercharge Your LLM with the Fastest KV Cache Layer

Streamer-Sales 销冠 —— 卖货主播 LLM 大模型??，一个能够根据给定的商品特点从激发用户购买意愿角度出发进行商品解说的卖货主播大模型。??内含详细的数据生成流程? ?另外还集成了 LMDeploy 加速推理?、RAG检索增强生成 ?、TTS文字转语音?、数字人生成 ?、 Agent 使用网络查询实时信息?、ASR 语音转文字??、Vue 生态搭建前端?、FastAPI 搭建后端??、Docker-compose 打包部署?

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

A PyTorch Library for Meta-learning Research

Keras Attention Layer (Luong and Bahdanau scores).

LiveStore is a next-generation state management framework based on reactive SQLite and built-in sync engine.

Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch

中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3

FSL-Mate: A collection of resources for few-shot learning (FSL).

Meta-Transformer for Unified Multimodal Learning

My personal prompt library for various LLMs + scripts & tools. Suitable for models from Deepseek, OpenAI, Claude, Meta, Mistral, Google, Grok, and others.

Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.