Popular GitHub Repositories for Code Llms

Get up and running with Llama 3.3, DeepSeek-R1, Phi-4, Gemma 3, and other large language models.

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

Elixir implementation of a LangChain style framework that lets Elixir projects integrate with and leverage LLMs.

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

GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.

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

A high-throughput and memory-efficient inference and serving engine for LLMs

CLI platform to experiment with codegen. Precursor to: https://lovable.dev

100 Days of ML Coding

The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, and more.

Finetune Llama 3.3, DeepSeek-R1 & Reasoning LLMs 2x faster with 70% less memory! ?

?「AI 低代码平台」前后端分离 SpringBoot 2.x/3.x，SpringCloud，Ant Design&Vue3，Mybatis，Shiro！强大的代码生成器让前后端代码一键生成，无需写任何代码! 引领AI低代码开发模式 AI生成->OnlineCoding->代码生成->手工MERGE，帮助Java项目解决80%重复工作，让开发更关注业务，提高开发效率、节省成本，同时又不失灵活性

Drag & drop UI to build your customized LLM flow

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

提取微信聊天记录，将其导出成HTML、Word、Excel文档永久保存，对聊天记录进行分析生成年度聊天报告，用聊天数据训练专属于个人的AI聊天助手

Low-code platform for building business applications. Connect to databases, cloud storages, GraphQL, API endpoints, Airtable, Google sheets, OpenAI, etc and build apps using drag and drop application builder. Built using JavaScript/TypeScript. ?

Enhancing code comprehension with AI-driven explanations and a secure code snippet repository.

AI's query engine - Platform for building AI that can learn and answer questions over large scale federated data.