AI Model2026
A 27B multimodal causal language model with a vision encoder and native long-context support (262,144 tokens). Optimized for repository-level coding agents and multimodal understanding; includes preserved "thinking" traces, multi-token prediction (MTP), and deployment recipes for vLLM / SGLang / Transformers.
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 Model2026
A vision-oriented foundation checkpoint for low-latency inference — DeepSeek V4 base in safetensors with FP8 optimizations. Designed for fast image generation and embedding use in inference pipelines; verify license and FP8/runtime compatibility before production use.
AI Model2026
Base image-generation foundation model tuned for visual search and prompt-guided synthesis, intended as a compact starting point for local inference or fine-tuning. Emphasizes easy integration into image pipelines and suitability for downstream adaptation.
AI Model2026
Unifies multimodal image understanding, text-to-image generation, and instruction-based editing in a single diffusion LLM using a Mixture-of-Experts backbone, SigLIP-VQ discrete tokenizer, and a distilled diffusion decoder enabling fast (8-step) decoding; full-generation needs ~47GB GPU RAM.
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
Provides an NVFP4-quantized 27B Qwen3.6 checkpoint optimized for faster, low-memory multimodal inference on 24GB GPUs. Includes MTP (multi-token prediction), extended 262k native context, and deployment recipes for vLLM/SGLang/KTransformers; best used with recommended backends for peak throughput.
AI Dataset2026
Provides a 30K+ problem multimodal, multilingual dataset of Olympiad-level math problems with expert solutions and a math-aware retrieval benchmark—includes images, hierarchical topics, provenance from official booklets, and LLM-assisted metadata (v0, CC BY 4.0).
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 Model2026
High-resolution vision transformers pretrained on one billion human images for human-centric tasks such as pose estimation, body-part segmentation, surface-normal and pointmap prediction. Provides multiple backbone sizes and task-specific checkpoints; released under the Sapiens2 license.
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.