Trains a 65M-parameter vision-language model from scratch in ~2 hours on one RTX 3090, about 3 RMB (~$0.40) of GPU rental. Connects a frozen SigLIP2 encoder to a small MiniMind LLM via a two-layer MLP projector; full PyTorch code for pretraining and SFT.
Turns PDFs and images into clean Markdown with a 7B vision-language model, keeping tables, equations, handwriting, and multi-column reading order while removing headers and footers. Runs on one 12GB+ GPU at about 1/32 the cost of GPT-4o APIs.
Official Python implementation of the Model Context Protocol. Build servers that expose tools, resources, and prompts to any MCP host, or clients that connect to any server; type hints and docstrings become the schemas, so a server fits in ~15 lines.
Converts PDFs, Office files, HTML, images and audio into one structured DoclingDocument, with deep PDF layout, reading order, table-structure and formula recognition, OCR, and native LangChain/LlamaIndex/Haystack integrations for RAG pipelines.
Reviews code in the IDE, CLI, and pull requests, flagging bugs, logic gaps, security holes, and missing tests using context from the whole repo and its dependencies. Enforces team-specific rules learned from past PRs.
A compact domain-specific language for writing high-performance GPU/CPU kernels (GEMM, FlashAttention, sparse kernels) with Python-like syntax. It provides tiling/pipelining primitives, a TVM-based compiler and multiple backends (CUDA/CuTeDSL, NVRTC, WebGPU, Metal, Ascend) for operator-level performance work.
Runs autonomous AI-agent workforces where each agent, skill, and company process lives as version-controlled code you own. Agents act in isolated sandboxes and submit deliverables for human review, with 3,000+ connectors plus MCP support.
Enables agents to autonomously operate GUIs and complete complex computer tasks — includes the Agent S papers and the gui-agents SDK, grounding-model support, and runnable S3 agent implementations for Windows/macOS/Linux.
Parses, generates, and filters training data from noisy sources like PDFs and weak QA, then feeds it into LLM pre-training, SFT, RL, or RAG cleaning. Ships 100+ operators and ready-made pipelines for text, reasoning, Text2SQL, and agentic data.
Python web scraping framework that automatically relocates elements when a site's HTML changes, so selectors survive redesigns. Bundles Cloudflare Turnstile bypass, TLS fingerprint impersonation, and a Scrapy-like async spider for full crawls.
VideoCaptioner is an AI-powered video subtitling assistant that combines ASR (local or cloud) with LLM-based subtitle segmentation, correction and translation. It supports offline GPU transcription, concurrent chunk transcription, VAD, speaker-aware processing, batch subtitling and one-click subtitle-to-video synthesis, with both GUI and CLI options.
Open-source HybridFlow implementation for RL post-training of LLMs. Decouples control flow from compute so PPO, GRPO, GSPO and DAPO share one dataflow; pairs FSDP/Megatron with vLLM/SGLang rollout and reports 1.5-20x throughput over prior RLHF stacks.