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.
AI Model2026
An HDR LoRA fine-tune for Lightricks' LTX-2.3 (22B) that enables image‑conditioned any‑to‑any image-to-video and text-to-video generation. Designed for HDR-aware synthesis workflows; requires the LTX-2.3 base model and a LoRA-capable runtime.
AI Model2026
FP8-quantized 27B multimodal Qwen3.6 model weights in Hugging Face Transformers format — supports image/text/video inputs, native 262k token context (extensible to ~1M), and is compatible with vLLM/SGLang/KTransformers for efficient local serving and research.
AI Dataset2026
Provides a large-scale multimodal embodied dataset (vision, depth, hand/arm kinematics, tactile) captured with an exoskeleton glove and egocentric sensors; organized as clip-level Zarr volumes for manipulation, imitation learning, and vision–action research. Includes both high-precision glove measurements and natural bare-hand clips; sizable storage required.
AI Model2026
Provides a locally runnable, refusal-free variant of Qwen3.6-27B with multiple K_P GGUF quantizations and mmproj multimodal support. The Aggressive flavor skips preambles on edgy prompts—use when you want direct/raw responses for local research, red‑teaming, or offline workflows.
AI Dataset2026
Provides instruction-based (before, after) structured 3D latents (SLAT) with aligned RGB views and natural-language edit prompts for training and evaluating instruction-following 3D editing models. Covers part-level semantic edits across seven edit types (deletion, addition, modification, scale, material, color, global) and supplies shard-based NPZ assets and loader code.
AI Model2026
End-to-end multimodal model for native text↔image understanding, interleaved image-text generation, and image editing. Uses the NEO-Unify MoT architecture to avoid separate visual encoders/VAE. Suited for multimodal prototyping, demos, and research (Apache‑2.0).
AI Model2026
A GGUF-format preview checkpoint derived from Qwen3.6-27B — a multimodal, image-text-to-text reasoning model fine-tuned for more structured reasoning and consistent answer style; packaged for local inference and compatible with engines like vLLM/SGLang/llama.cpp.
AI Dataset2026
Provides satellite image tiles paired with per-tile land-cover captions and bounding-box overlays in SFT-compatible JSONL for supervised fine-tuning. Includes RGB chips, optional Mapbox context, metadata, and train/validation/test splits derived from Sentinel‑2 and Earth Engine labels.