Combines drag-and-drop field binding with natural-language prompts so an AI agent derives the transformations behind charts your raw tables can't produce. Reads from databases, files, images, and websites; 30+ chart types and branchable threads.
Provides a Python framework for building generative-AI agents and workflows with Pydantic-style type safety and composable capabilities. Model-agnostic provider support, built-in observability, human-in-the-loop tool approval, and durable execution for production use cases.
Provides code, pretrained weights, and tooling for protein language models and structure prediction — including ESMC, ESMFold2, sparse autoencoders (SAEs), and the ESM Atlas. Includes model checkpoints, tutorials, Hugging Face & Biohub integration, and an MIT license.
Disaggregated LLM serving architecture that splits prefill and decode into separate clusters and pools spare CPU, DRAM, and SSD into a distributed KVCache. Powers Kimi in production, handling 75% more requests under the same SLOs.
Official code companion to the O'Reilly book by Jay Alammar and Maarten Grootendorst: 12 chapters of runnable notebooks on tokens, embeddings, Transformers, text classification, clustering, prompt engineering, semantic search, RAG, and fine-tuning.
Crawls 30+ social platforms (Weibo, Xiaohongshu, Douyin), parses their video and image content, then has five specialized agents debate in a moderated forum to synthesize public-opinion reports. Can fuse public sentiment with a private business database.
Open-source TTS that clones a voice from 3-10s of audio and synthesizes cross-lingual speech in 9 languages and 18+ Chinese dialects. Supports streaming at ~150ms latency with instruction control over emotion, speed, and accent.
Routes each user query to the most suitable agent via a classifier that weighs agent profiles and conversation history, keeping context shared across handoffs. Python and TypeScript, with a SupervisorAgent that runs sub-agents in parallel.
Runs huge mixture-of-experts LLMs like DeepSeek-R1/V3 on a single 24GB GPU plus CPU DRAM by keeping attention on the GPU and offloading expert weights to CPU. Reports 3-28x speedups via Intel AMX/AVX512 kernels and fits 139K context in 24GB VRAM.
Official inference framework for 1-bit and ternary (1.58-bit) LLMs such as BitNet b1.58, with optimized CPU kernels. Delivers 1.37x-6.17x speedups and 55-82% lower energy on x86 and ARM, and runs a 100B model on a single CPU at 5-7 tokens/sec.
Developer framework for building AI agents that autonomously trade on Polymarket prediction markets. Bundles the Polymarket and Gamma APIs, a Chroma RAG layer that pulls in news, and a CLI to query markets, reason with an LLM, and execute trades.
Stores agent memory as human- and agent-readable Markdown files with wikilinks instead of an opaque vector DB. Auto Memory/Resource/Dream jobs distill conversations into long-term notes, and hybrid wikilink + BM25 + embedding search retrieves them.