Creates personalized digital avatars (AI twins) by fine-tuning LLMs on users' chat history and binding them to chatbots. Provides an end-to-end pipeline — chat export, preprocessing with privacy filters, SFT/LoRA training, and deployment (Telegram/Discord/Slack). Best with larger models and substantial chat data.
Asynchronous, reverse-engineered Python API for programmatic access to the Google Gemini web app — supports persistent cookie auth, streaming text, image/video/audio generation, deep-research workflows, model selection, and a CLI for automation and chatbots.
Ingests documents, images, audio, video and web pages and converts them into structured, LLM-friendly markdown and parsed data. Runs locally (fits on a T4 GPU), supports ~20 file types, offers OCR, transcription, table extraction and a Gradio UI; deployable via Docker/Skypilot. Licensed under GPL-3.0; some model weights carry cc-by-nc-sa restrictions for commercial use.
A research codebase and model family for vision–language models that experiments with data‑centric post‑training strategies and long‑context multimodal reasoning. Includes model reports, released research weights (non‑commercial), grounding tools (LocateAnything) and integrations for inference/optimization.
Provides a 1 trillion-token multimodal interleaved dataset (HTML subset updated as data_v1_1 with 742B HTML tokens) and 3.4B images drawn from HTML/PDF/ArXiv sources for multimodal pretraining; released under CC-BY-4.0 with safety and deduplication guidance.
Provides point-accurate annotations of interactive parts in high-resolution indoor laser-scan point clouds, plus affordance labels, motion axes and natural-language task descriptions; includes aligned iPad RGB-D video slices with 2D projections for multimodal research.
High-resolution image and video generation codebase and models that run with far lower compute and memory than typical diffusion systems. Uses linear-attention DiT variants, aggressive latent compression, and inference-scaling to support text-to-image (up to 4K), fast one/few-step generation, and efficient video pipelines.
Reference architectures and microservices for building GPU-accelerated vision agents that enable natural-language video search, long-video summarization, visual Q&A, and alert verification. Integrates NVIDIA NIM models, embeddings, VLMs/LLMs, and agent workflows for deployable video-analytics stacks.
Unifies visual creator tools, reusable Skills, and a Codex-powered Agent to handle video translation, image/video generation, voiceovers, and editing in one local multimodal workspace. Desktop and web clients share a synchronized state machine and keep data local by default.
Provides an open platform of omnimodal world models, datasets, and tools to build Physical AI — joint perception, generation, and action reasoning for robots, autonomous vehicles, and smart infrastructure. Supports images, video, audio, and action-conditioned workflows.
Generates full songs from lyrics and style prompts by first writing an editable melody-and-chord score and then rendering vocals and accompaniment. Unifies symbolic planning and audio synthesis in one pipeline, enabling readable score inspection, zero‑shot covers, and agent-driven editing.
Multi‑modal closed-ended academic benchmark with 2,500 multiple-choice and short-answer exam questions spanning math, natural sciences, and humanities for automated grading. Curated by subject-matter experts, released under MIT, and includes a canary string to help prevent dataset leakage into model training.