A programming framework for agentic AI 🤖

Use PEFT or Full-parameter to finetune 350+ LLMs or 100+ MLLMs. (LLM: Qwen2.5, Llama3.2, GLM4, Internlm2.5, Yi1.5, Mistral, Baichuan2, DeepSeek, Gemma2, ...; MLLM: Qwen2-VL, Qwen2-Audio, Llama3.2-V…

Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.

An open source tool to quantify the world

Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM 等语言模型的本地知识库问答 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM) QA app with langchain

吴恩达大模型系列课程中文版，包括《Prompt Engineering》、《Building System》和《LangChain》

⚡ Building applications with LLMs through composability ⚡

Source code for Twitter's Recommendation Algorithm

中英文敏感词、语言检测、中外手机/电话归属地/运营商查询、名字推断性别、手机号抽取、身份证抽取、邮箱抽取、中日文人名库、中文缩写库、拆字词典、词汇情感值、停用词、反动词表、暴恐词表、繁简体转换、英文模拟中文发音、汪峰歌词生成器、职业名称词库、同义词库、反义词库、否定词库、汽车品牌词库、汽车零件词库、连续英文切割、各种中文词向量、公司名字大全、古诗词库、IT词库、财经词库、成语词库、地名词库、…

CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).

tensorflow实战练习，包括强化学习、推荐系统、nlp等

《剑指Offer》题目汇总&常考题总结(Python实现)

Learn Node Embeddings from DeepWalk

云原生一站式机器学习平台，oa，项目组权限，在线开发，分布式训练，超参搜索，推理服务，多集群调度，多项目组资源组，边缘计算

Implementation and experiments of graph embedding algorithms.deep walk,LINE(Large-scale Information Network Embedding),node2vec,SDNE(Structural Deep Network Embedding),struc2vec

Contrastive Language-Image Pretraining

Chinese NewsTitle Generation Project by GPT2.带有超级详细注释的中文GPT2新闻标题生成项目。

Kubernetes-native Deep Learning Framework

train, evaluate and deploy model using tf-estimator

深度学习相关的模型训练、评估和预测相关代码

推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction

A deep matching model library for recommendations, advertising, and search. It's easy to train models and to export representation vectors for user and item which can be used for ANN search.

light reimplement of bert for keras

Source code and dataset for KDD 2019 paper "Representation Learning for Attributed Multiplex Heterogeneous Network"