Popular GitHub Repositories for Data Description Language

Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.

? Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

21 Lessons, Get Started Building with Generative AI ? https://microsoft.github.io/generative-ai-for-beginners/

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。

For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine.

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, moss等。

? Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 80+ languages.

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

Deep Learning for humans

? Empowering everyone to build reliable and efficient smart contracts.

A natural language interface for computers

? The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming

1 min voice data can also be used to train a good TTS model! (few shot voice cloning)

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. ?Docker-friendly.?Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

Drag & drop UI to build your customized LLM flow

LlamaIndex is the leading framework for building LLM-powered agents over your data.

Learn how to design, develop, deploy and iterate on production-grade ML applications.