A large language model (LLM) is a type of machine learning model designed for understanding, generating, and interacting with human language. These models are trained on extensive datasets containing text from books, articles, websites, and other sources to learn patterns, context, and semantics in language. LLMs are widely used in applications like chatbots, code generation, translation, summarization, and more. They are often built using transformer architectures and are central to the field of generative AI.
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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
The agent engineering platform.
利用 AI 大模型和自动化工作流，根据主题或关键词一键生成高清短视频。Generate HD short videos from a topic or keyword with an automated AI workflow.
Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.
Agents that use the browser.
TradingAgents: Multi-Agents LLM Financial Trading Framework
A high-throughput and memory-efficient inference and serving engine for LLMs
Open-source web crawler and scraper for LLMs and AI agents: any website into clean, LLM-ready Markdown. Run it yourself, or use Crawl4AI Cloud with one key.
An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1
Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.
LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs.