AI Infra2026
Compiles raw documents into a persistent, interlinked Markdown wiki that LLMs can query; uses PageIndex for vectorless, reasoning-based retrieval of long documents, supports native multi-modality, bundled web Workbench, and skill distillation.
AI Model2026
Multimodal image-text-to-text fork of Gemma 4 (31B) using a 'CRACK v2' abliteration — tuned for conversational vision inputs and thinking-mode support in JANG v2 safetensors format. Recommended to run in vMLX; published by dealignai.
AI Model2026
Generates persistent, explorable 3D worlds from a single image by synthesizing long-range, geometry-consistent video and reconstructing it into an explicit 3D Gaussian scene. Intended for internal research use under NVIDIA's research license.
AI Dataset2026
Provides a ~9.2M-instance Japanese multimodal post-training dataset for vision–language models, combining image–text pairs, PDF corpora and generated VQA to improve Japanese VLM performance; access is restricted by Japanese copyright (download via llm-jp GitLab).
AI Dataset2026
Large-scale in-the-wild robot manipulation dataset with ~76K teleoperated trajectories (~350 hours) that provides synchronized multi-view video, depth, camera calibration, robot state/action traces, and natural-language task instructions to train and evaluate manipulation policies and dynamics models. Collected across 564 scenes, 86 tasks, 52 buildings, on a uniform Franka Panda hardware stack and released in LeRobotDataset v3.0 format (≈707 GB, OpenMDW1.1).
AI Model2026
Delivers an ultra-efficient, edge-friendly multimodal image-and-video-to-text model optimized for on-device deployment. Uses mixed 4x/16x visual token compression, a low-FLOPs visual encoder, and multiple quantized variants for mobile and embedded inference.
AI Dataset2026
Provides paired before/after satellite images with question–answer annotations for semantic change understanding. Includes Yes/No and multiple-choice formats, delivered in Hugging Face datasets (streaming-friendly), suited for remote-sensing multimodal VQA and semantic change captioning research.
AI Model2026
Unifies multimodal understanding, reasoning, and image generation in a single end-to-end architecture using the NEO-unify paradigm. Models pixels and words jointly without a separate visual encoder, and provides interleaved image–text generation, infographic editing, and GGUF/low‑VRAM inference options.
AI Dataset2026
About 9,700 synthetic full-page web screenshots with YOLO-format, pixel-aligned bounding boxes for 14 UI element classes, generated by LLM-augmented HTML and Playwright DOM extraction. Includes CC3M image injection to reduce visual gap; released for non-commercial research (CC BY-NC-SA 4.0).
AI Model2026
Provides a GGUF-packaged, native-INT4 quantized build of the multimodal Kimi K2.6 model for image-text-to-text inference — packaged for local/self-hosted inference engines (vLLM, SGLang, KTransformers) to reduce footprint while keeping multimodal capabilities.
AI Dataset2026
A 1.4M image–text style dataset for text-to-image generation and style transfer, produced by mapping 170K curated style prompts to 400K content prompts via Qwen-Image to yield strong intra-style consistency. Designed for training and evaluating style-aware generative models; license: other.
AI Model2026
Unified multimodal LLM for enterprise workflows: ingests video, audio, image and text to perform transcription, OCR, Q&A, summarization and long-context reasoning. Provides BF16/FP8/NVFP4 weights and integrations with vLLM, TensorRT-LLM and other runtimes.