AI Model2026
A 33B Mixture-of-Experts text-to-text model optimized for local, long-context agentic coding—3B activated params per token, 131k token window, mixed sliding-window and global attention, FP8 KV cache, Apache-2.0 license.
AI Model2026
Unifies video, audio, image and text understanding for enterprise Q&A, summarization, transcription and document intelligence. The NVFP4 quantized variant reduces footprint to ~20.9GB for more efficient single‑GPU deployment and is tuned for NVIDIA runtimes (vLLM, TensorRT).
AI Model2026
Provides unquantized BF16 weights of Qwen3.6-27B with the base model's MTP head grafted in for high-fidelity, uncensored text (and multimodal) generation. Includes deployment guidance and hardware-tuned variants for A100/H100 and Blackwell-class GPUs.
AI Model2026
Provides a GGUF-quantized build of NVIDIA's Nemotron 3 Nano Omni 30B (Reasoning) for local inference — enables multimodal (video/audio/image/text) reasoning, transcription, and document understanding on compatible runtimes such as llama.cpp, Ollama, vLLM, and TensorRT-LLM.
AI Dataset2026
Collection of 76 image-centric multimodal subdatasets (≈6.9M samples, ~39.56B estimated tokens) for training vision–language models, each published with a standardized conversation JSONL and dataset card. Media are referenced by path/URL and must be fetched separately; licensing is primarily CC-BY-4.0 with per-subdataset variations.
AI Model2026
Converts latent representations into high-resolution images by using a conditional pixel-space diffusion decoder that merges decoding and upsampling into a single generative step. Released checkpoints are 4-step distilled (2k and 2kto4k variants) and pair with specific VAE/encoder weights; license restricts use to non-commercial research.
AI Dataset2026
Large-scale synthetic video dataset of 236,937 1080p clips (≈5,841 hours) of digital humans with per-frame metric depth and camera parameters — built as a controllable supplement for world-model pretraining, camera-motion generalization, and geometry-aware physical-AI research.
AI Dataset2026
Large-scale synthetic video dataset of physically simulated multi-object interaction scenes for training and evaluating models on physical reasoning, depth and optical-flow estimation, instance segmentation, and physics-grounded captioning. Provides RGB + lossless depth, per-frame instance masks, per-object physics annotations (NPZ), VLM-grounded captions, and USD scene files — useful for world-model and simulation-to-real work; commercial use permitted.
AI Model2026
Multilingual streaming ASR that transcribes 40 language-locales using a cache-aware FastConformer‑RNNT architecture. Supports language-ID prompting (or auto-detect), punctuation/capitalization, and configurable chunk sizes to trade latency vs. accuracy for production transcription and streaming voice agents.
AI Model2026
Generates temporally coherent MP4 videos from a single input image plus text instructions, with configurable resolution, frame count, and optional AAC audio. Optimized for NVIDIA GPU stacks and integrates with vLLM‑Omni and Hugging Face Diffusers for production inference and research workflows.
AI Model2026
A ternary-weight (~1.58-bit) 4B text-to-image diffusion transformer optimized for NVIDIA GPUs using Gemlite INT2 and HQQ; it reduces the transformer to ~1.21 GB (4.55 GB CUDA payload) and targets 1024×1024 generation with a 4-step FlowMatch-Euler sampler.
AI Model2026
Performs fast, high-quality vision–language grounding: given an image plus a natural-language prompt it returns bounding boxes or points for referred objects. Uses Parallel Box Decoding for parallel coordinate prediction (higher throughput) and targets research/non-commercial use.